{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Batch Normalization\n",
    "One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. \n",
    "One idea along these lines is batch normalization which was proposed by [1] in 2015.\n",
    "\n",
    "The idea is relatively straightforward. Machine learning methods tend to work better when their input data consists of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features; this will ensure that the first layer of the network sees data that follows a nice distribution. However, even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.\n",
    "\n",
    "The authors of [1] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, [1] proposes to insert batch normalization layers into the network. At training time, a batch normalization layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.\n",
    "\n",
    "It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.\n",
    "\n",
    "[1] [Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n",
    "Internal Covariate Shift\", ICML 2015.](https://arxiv.org/abs/1502.03167)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========== You can safely ignore the message below if you are NOT working on ConvolutionalNetworks.ipynb ===========\n",
      "\tYou will need to compile a Cython extension for a portion of this assignment.\n",
      "\tThe instructions to do this will be given in a section of the notebook below.\n",
      "\tThere will be an option for Colab users and another for Jupyter (local) users.\n"
     ]
    }
   ],
   "source": [
    "# As usual, a bit of setup\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\" returns relative error \"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))\n",
    "\n",
    "def print_mean_std(x,axis=0):\n",
    "    print('  means: ', x.mean(axis=axis))\n",
    "    print('  stds:  ', x.std(axis=axis))\n",
    "    print() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train:  (49000, 3, 32, 32)\n",
      "y_train:  (49000,)\n",
      "X_val:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "  print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: forward\n",
    "In the file `cs231n/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation.\n",
    "\n",
    "Referencing the paper linked to above in [1] may be helpful!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before batch normalization:\n",
      "  means:  [ -2.3814598  -13.18038246   1.91780462]\n",
      "  stds:   [27.18502186 34.21455511 37.68611762]\n",
      "\n",
      "After batch normalization (gamma=1, beta=0)\n",
      "  means:  [2.22044605e-17 8.16013923e-17 4.57966998e-17]\n",
      "  stds:   [0.99999999 1.         1.        ]\n",
      "\n",
      "After batch normalization (gamma= [1. 2. 3.] , beta= [11. 12. 13.] )\n",
      "  means:  [11. 12. 13.]\n",
      "  stds:   [0.99999999 1.99999999 2.99999999]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after batch normalization   \n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before batch normalization:')\n",
    "print_mean_std(a,axis=0)\n",
    "\n",
    "gamma = np.ones((D3,))\n",
    "beta = np.zeros((D3,))\n",
    "# Means should be close to zero and stds close to one\n",
    "print('After batch normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=0)\n",
    "\n",
    "gamma = np.asarray([1.0, 2.0, 3.0])\n",
    "beta = np.asarray([11.0, 12.0, 13.0])\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "print('After batch normalization (gamma=', gamma, ', beta=', beta, ')')\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After batch normalization (test-time):\n",
      "  means:  [-0.03927354 -0.04349152 -0.10452688]\n",
      "  stds:   [1.01531427 1.01238373 0.97819987]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "\n",
    "for t in range(50):\n",
    "  X = np.random.randn(N, D1)\n",
    "  a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "  batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "bn_param['mode'] = 'test'\n",
    "X = np.random.randn(N, D1)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print('After batch normalization (test-time):')\n",
    "print_mean_std(a_norm,axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: backward\n",
    "Now implement the backward pass for batch normalization in the function `batchnorm_backward`.\n",
    "\n",
    "To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.\n",
    "\n",
    "Once you have finished, run the following to numerically check your backward pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  1.702926968594948e-09\n",
      "dgamma error:  7.418819628470949e-13\n",
      "dbeta error:  2.8795057655839487e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: batchnorm_forward(x, a, beta, bn_param)[0]\n",
    "fb = lambda b: batchnorm_forward(x, gamma, b, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = batchnorm_backward(dout, cache)\n",
    "#You should expect to see relative errors between 1e-13 and 1e-8\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: alternative backward\n",
    "In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For example, you can derive a very simple formula for the sigmoid function's backward pass by simplifying gradients on paper.\n",
    "\n",
    "Surprisingly, it turns out that you can do a similar simplification for the batch normalization backward pass too!  \n",
    "\n",
    "In the forward pass, given a set of inputs $X=\\begin{bmatrix}x_1\\\\x_2\\\\...\\\\x_N\\end{bmatrix}$, \n",
    "\n",
    "we first calculate the mean $\\mu$ and variance $v$.\n",
    "With $\\mu$ and $v$ calculated, we can calculate the standard deviation $\\sigma$  and normalized data $Y$.\n",
    "The equations and graph illustration below describe the computation ($y_i$ is the i-th element of the vector $Y$).\n",
    "\n",
    "\\begin{align}\n",
    "& \\mu=\\frac{1}{N}\\sum_{k=1}^N x_k  &  v=\\frac{1}{N}\\sum_{k=1}^N (x_k-\\mu)^2 \\\\\n",
    "& \\sigma=\\sqrt{v+\\epsilon}         &  y_i=\\frac{x_i-\\mu}{\\sigma}\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://raw.githubusercontent.com/cs231n/cs231n.github.io/master/assets/a2/batchnorm_graph.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "The meat of our problem during backpropagation is to compute $\\frac{\\partial L}{\\partial X}$, given the upstream gradient we receive, $\\frac{\\partial L}{\\partial Y}.$ To do this, recall the chain rule in calculus gives us $\\frac{\\partial L}{\\partial X} = \\frac{\\partial L}{\\partial Y} \\cdot \\frac{\\partial Y}{\\partial X}$.\n",
    "\n",
    "The unknown/hart part is $\\frac{\\partial Y}{\\partial X}$. We can find this by first deriving step-by-step our local gradients at \n",
    "$\\frac{\\partial v}{\\partial X}$, $\\frac{\\partial \\mu}{\\partial X}$,\n",
    "$\\frac{\\partial \\sigma}{\\partial v}$, \n",
    "$\\frac{\\partial Y}{\\partial \\sigma}$, and $\\frac{\\partial Y}{\\partial \\mu}$,\n",
    "and then use the chain rule to compose these gradients (which appear in the form of vectors!) appropriately to compute $\\frac{\\partial Y}{\\partial X}$.\n",
    "\n",
    "If it's challenging to directly reason about the gradients over $X$ and $Y$ which require matrix multiplication, try reasoning about the gradients in terms of individual elements $x_i$ and $y_i$ first: in that case, you will need to come up with the derivations for $\\frac{\\partial L}{\\partial x_i}$, by relying on the Chain Rule to first calculate the intermediate $\\frac{\\partial \\mu}{\\partial x_i}, \\frac{\\partial v}{\\partial x_i}, \\frac{\\partial \\sigma}{\\partial x_i},$ then assemble these pieces to calculate $\\frac{\\partial y_i}{\\partial x_i}$. \n",
    "\n",
    "You should make sure each of the intermediary gradient derivations are all as simplified as possible, for ease of implementation. \n",
    "\n",
    "After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx difference:  0.0\n",
      "dgamma difference:  0.0\n",
      "dbeta difference:  0.0\n",
      "speedup: 1.00x\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D = 100, 500\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "out, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "t1 = time.time()\n",
    "dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)\n",
    "t2 = time.time()\n",
    "dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)\n",
    "t3 = time.time()\n",
    "\n",
    "print('dx difference: ', rel_error(dx1, dx2))\n",
    "print('dgamma difference: ', rel_error(dgamma1, dgamma2))\n",
    "print('dbeta difference: ', rel_error(dbeta1, dbeta2))\n",
    "print('speedup: %.2fx' % ((t2 - t1) / (t3 - t2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fully Connected Nets with Batch Normalization\n",
    "Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs231n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.\n",
    "\n",
    "Concretely, when the `normalization` flag is set to `\"batchnorm\"` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.\n",
    "\n",
    "HINT: You might find it useful to define an additional helper layer similar to those in the file `cs231n/layer_utils.py`. If you decide to do so, do it in the file `cs231n/classifiers/fc_net.py`.\n",
    "\n",
    "- **添加了affine_bn_relu_forward()和affine_bn_relu_backward()方法**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.2611955101340957\n",
      "W1 relative error: 1.10e-04\n",
      "W2 relative error: 2.85e-06\n",
      "W3 relative error: 3.92e-10\n",
      "b1 relative error: 5.55e-09\n",
      "b2 relative error: 2.22e-08\n",
      "b3 relative error: 4.78e-11\n",
      "beta1 relative error: 7.33e-09\n",
      "beta2 relative error: 1.89e-09\n",
      "gamma1 relative error: 7.57e-09\n",
      "gamma2 relative error: 1.96e-09\n",
      "\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  6.996533220108303\n",
      "W1 relative error: 1.98e-06\n",
      "W2 relative error: 2.28e-06\n",
      "W3 relative error: 1.11e-08\n",
      "b1 relative error: 2.78e-09\n",
      "b2 relative error: 5.55e-09\n",
      "b3 relative error: 2.23e-10\n",
      "beta1 relative error: 6.65e-09\n",
      "beta2 relative error: 5.69e-09\n",
      "gamma1 relative error: 8.80e-09\n",
      "gamma2 relative error: 4.14e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "# You should expect losses between 1e-4~1e-10 for W, \n",
    "# losses between 1e-08~1e-10 for b,\n",
    "# and losses between 1e-08~1e-09 for beta and gammas.\n",
    "for reg in [0, 3.14]:\n",
    "  print('Running check with reg = ', reg)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64,\n",
    "                            normalization='batchnorm')\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n",
    "  if reg == 0: print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batchnorm for deep networks\n",
    "Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solver with batch norm:\n",
      "(Iteration 1 / 200) loss: 2.340974\n",
      "(Epoch 0 / 10) train acc: 0.107000; val_acc: 0.115000\n",
      "(Epoch 1 / 10) train acc: 0.314000; val_acc: 0.266000\n",
      "(Iteration 21 / 200) loss: 2.039365\n",
      "(Epoch 2 / 10) train acc: 0.390000; val_acc: 0.279000\n",
      "(Iteration 41 / 200) loss: 2.036710\n",
      "(Epoch 3 / 10) train acc: 0.497000; val_acc: 0.316000\n",
      "(Iteration 61 / 200) loss: 1.769763\n",
      "(Epoch 4 / 10) train acc: 0.532000; val_acc: 0.311000\n",
      "(Iteration 81 / 200) loss: 1.271362\n",
      "(Epoch 5 / 10) train acc: 0.594000; val_acc: 0.325000\n",
      "(Iteration 101 / 200) loss: 1.279818\n",
      "(Epoch 6 / 10) train acc: 0.654000; val_acc: 0.331000\n",
      "(Iteration 121 / 200) loss: 1.071662\n",
      "(Epoch 7 / 10) train acc: 0.675000; val_acc: 0.334000\n",
      "(Iteration 141 / 200) loss: 1.293454\n",
      "(Epoch 8 / 10) train acc: 0.737000; val_acc: 0.320000\n",
      "(Iteration 161 / 200) loss: 0.760773\n",
      "(Epoch 9 / 10) train acc: 0.767000; val_acc: 0.318000\n",
      "(Iteration 181 / 200) loss: 0.853528\n",
      "(Epoch 10 / 10) train acc: 0.746000; val_acc: 0.315000\n",
      "\n",
      "Solver without batch norm:\n",
      "(Iteration 1 / 200) loss: 2.302332\n",
      "(Epoch 0 / 10) train acc: 0.129000; val_acc: 0.131000\n",
      "(Epoch 1 / 10) train acc: 0.283000; val_acc: 0.250000\n",
      "(Iteration 21 / 200) loss: 2.041970\n",
      "(Epoch 2 / 10) train acc: 0.316000; val_acc: 0.277000\n",
      "(Iteration 41 / 200) loss: 1.900473\n",
      "(Epoch 3 / 10) train acc: 0.373000; val_acc: 0.282000\n",
      "(Iteration 61 / 200) loss: 1.713156\n",
      "(Epoch 4 / 10) train acc: 0.390000; val_acc: 0.310000\n",
      "(Iteration 81 / 200) loss: 1.662208\n",
      "(Epoch 5 / 10) train acc: 0.433000; val_acc: 0.300000\n",
      "(Iteration 101 / 200) loss: 1.696236\n",
      "(Epoch 6 / 10) train acc: 0.529000; val_acc: 0.345000\n",
      "(Iteration 121 / 200) loss: 1.555476\n",
      "(Epoch 7 / 10) train acc: 0.536000; val_acc: 0.301000\n",
      "(Iteration 141 / 200) loss: 1.434566\n",
      "(Epoch 8 / 10) train acc: 0.612000; val_acc: 0.328000\n",
      "(Iteration 161 / 200) loss: 1.052030\n",
      "(Epoch 9 / 10) train acc: 0.655000; val_acc: 0.333000\n",
      "(Iteration 181 / 200) loss: 0.796547\n",
      "(Epoch 10 / 10) train acc: 0.726000; val_acc: 0.324000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [100, 100, 100, 100, 100]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 2e-2\n",
    "bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n",
    "model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "\n",
    "print('Solver with batch norm:')\n",
    "bn_solver = Solver(bn_model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True,print_every=20)\n",
    "bn_solver.train()\n",
    "\n",
    "print('\\nSolver without batch norm:')\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=20)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following to visualize the results from two networks trained above. You should find that using batch normalization helps the network to converge much faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_training_history(title, label, baseline, bn_solvers, plot_fn, bl_marker='.', bn_marker='.', labels=None):\n",
    "    \"\"\"utility function for plotting training history\"\"\"\n",
    "    plt.title(title)\n",
    "    plt.xlabel(label)\n",
    "    bn_plots = [plot_fn(bn_solver) for bn_solver in bn_solvers]\n",
    "    bl_plot = plot_fn(baseline)\n",
    "    num_bn = len(bn_plots)\n",
    "    for i in range(num_bn):\n",
    "        label='with_norm'\n",
    "        if labels is not None:\n",
    "            label += str(labels[i])\n",
    "        plt.plot(bn_plots[i], bn_marker, label=label)\n",
    "    label='baseline'\n",
    "    if labels is not None:\n",
    "        label += str(labels[0])\n",
    "    plt.plot(bl_plot, bl_marker, label=label)\n",
    "    plt.legend(loc='lower center', ncol=num_bn+1) \n",
    "\n",
    "    \n",
    "plt.subplot(3, 1, 1)\n",
    "plot_training_history('Training loss','Iteration', solver, [bn_solver], \\\n",
    "                      lambda x: x.loss_history, bl_marker='o', bn_marker='o')\n",
    "plt.subplot(3, 1, 2)\n",
    "plot_training_history('Training accuracy','Epoch', solver, [bn_solver], \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-o', bn_marker='-o')\n",
    "plt.subplot(3, 1, 3)\n",
    "plot_training_history('Validation accuracy','Epoch', solver, [bn_solver], \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-o', bn_marker='-o')\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch normalization and initialization\n",
    "We will now run a small experiment to study the interaction of batch normalization and weight initialization.\n",
    "\n",
    "The first cell will train 8-layer networks both with and without batch normalization using different scales for weight initialization. The second layer (cell ?) will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running weight scale 1 / 20\n",
      "Running weight scale 2 / 20\n",
      "Running weight scale 3 / 20\n",
      "Running weight scale 4 / 20\n",
      "Running weight scale 5 / 20\n",
      "Running weight scale 6 / 20\n",
      "Running weight scale 7 / 20\n",
      "Running weight scale 8 / 20\n",
      "Running weight scale 9 / 20\n",
      "Running weight scale 10 / 20\n",
      "Running weight scale 11 / 20\n",
      "Running weight scale 12 / 20\n",
      "Running weight scale 13 / 20\n",
      "Running weight scale 14 / 20\n",
      "Running weight scale 15 / 20\n",
      "Running weight scale 16 / 20\n",
      "Running weight scale 17 / 20\n",
      "Running weight scale 18 / 20\n",
      "Running weight scale 19 / 20\n",
      "Running weight scale 20 / 20\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [50, 50, 50, 50, 50, 50, 50]\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "bn_solvers_ws = {}\n",
    "solvers_ws = {}\n",
    "weight_scales = np.logspace(-4, 0, num=20)\n",
    "for i, weight_scale in enumerate(weight_scales):\n",
    "    print('Running weight scale %d / %d' % (i + 1, len(weight_scales)))\n",
    "    bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n",
    "    model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "\n",
    "    bn_solver = Solver(bn_model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "    bn_solver.train()\n",
    "    bn_solvers_ws[weight_scale] = bn_solver\n",
    "\n",
    "    solver = Solver(model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "    solver.train()\n",
    "    solvers_ws[weight_scale] = solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results of weight scale experiment\n",
    "best_train_accs, bn_best_train_accs = [], []\n",
    "best_val_accs, bn_best_val_accs = [], []\n",
    "final_train_loss, bn_final_train_loss = [], []\n",
    "\n",
    "for ws in weight_scales:\n",
    "    best_train_accs.append(max(solvers_ws[ws].train_acc_history))\n",
    "    bn_best_train_accs.append(max(bn_solvers_ws[ws].train_acc_history))\n",
    "  \n",
    "    best_val_accs.append(max(solvers_ws[ws].val_acc_history))\n",
    "    bn_best_val_accs.append(max(bn_solvers_ws[ws].val_acc_history))\n",
    "  \n",
    "    final_train_loss.append(np.mean(solvers_ws[ws].loss_history[-100:]))\n",
    "    bn_final_train_loss.append(np.mean(bn_solvers_ws[ws].loss_history[-100:]))\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Best val accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best val accuracy')\n",
    "plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Best train accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best training accuracy')\n",
    "plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Final training loss vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Final training loss')\n",
    "plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "plt.gca().set_ylim(1.0, 3.5)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 1:\n",
    "Describe the results of this experiment. How does the scale of weight initialization affect models with/without batch normalization differently, and why?\n",
    "\n",
    "## Answer:\n",
    "\n",
    "- 相比baseline网络，BN层网络对weight scale更不敏感，weight scale可调范围较大！\n",
    "- baseline网络与BN网络在验证集上准确率效果却是差不多的\n",
    "- BN网络能在较大的weight scale范围内达到比baseline网络更低的loss值，而且没有**loss爆炸？**\n",
    "\n",
    "\n",
    "- BN网络对weight scale适应性更好的原因可能是：\n",
    "  - BN对上一层的输出都进行标准化（使得输入下一层时是近似0均值和1方差的）再还原，缓解了weight scale对网络的影响但又保留了网络对数据分布的表现能力  \n",
    "  - 同时它，梯度下降的方向对全空间覆盖更好（数据分布偏移累加到最后可能导致梯度方向不好，比如梯度各分量同号），从而能收敛更快/找到更优解\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch normalization and batch size\n",
    "We will now run a small experiment to study the interaction of batch normalization and batch size.\n",
    "\n",
    "The first cell will train 6-layer networks both with and without batch normalization using different batch sizes. The second layer (cell ?) will plot training accuracy and validation set accuracy over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No normalization: batch size =  5\n",
      "Normalization: batch size =  5\n",
      "Normalization: batch size =  10\n",
      "Normalization: batch size =  50\n"
     ]
    }
   ],
   "source": [
    "def run_batchsize_experiments(normalization_mode):\n",
    "    np.random.seed(231)\n",
    "    # Try training a very deep net with batchnorm\n",
    "    hidden_dims = [100, 100, 100, 100, 100]\n",
    "    num_train = 1000\n",
    "    small_data = {\n",
    "      'X_train': data['X_train'][:num_train],\n",
    "      'y_train': data['y_train'][:num_train],\n",
    "      'X_val': data['X_val'],\n",
    "      'y_val': data['y_val'],\n",
    "    }\n",
    "    n_epochs=10\n",
    "    weight_scale = 2e-2\n",
    "    batch_sizes = [5,10,50]\n",
    "    lr = 10**(-3.5)\n",
    "    solver_bsize = batch_sizes[0]\n",
    "\n",
    "    print('No normalization: batch size = ',solver_bsize)\n",
    "    model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "    solver = Solver(model, small_data,\n",
    "                    num_epochs=n_epochs, batch_size=solver_bsize,\n",
    "                    update_rule='adam',\n",
    "                    optim_config={\n",
    "                      'learning_rate': lr,\n",
    "                    },\n",
    "                    verbose=False)\n",
    "    solver.train()\n",
    "    \n",
    "    bn_solvers = []\n",
    "    for i in range(len(batch_sizes)):\n",
    "        b_size=batch_sizes[i]\n",
    "        print('Normalization: batch size = ',b_size)\n",
    "        bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=normalization_mode)\n",
    "        bn_solver = Solver(bn_model, small_data,\n",
    "                        num_epochs=n_epochs, batch_size=b_size,\n",
    "                        update_rule='adam',\n",
    "                        optim_config={\n",
    "                          'learning_rate': lr,\n",
    "                        },\n",
    "                        verbose=False)\n",
    "        bn_solver.train()\n",
    "        bn_solvers.append(bn_solver)\n",
    "        \n",
    "    return bn_solvers, solver, batch_sizes\n",
    "\n",
    "batch_sizes = [5,10,50]\n",
    "bn_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('batchnorm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3QAAAJcCAYAAACxGOZUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3Xl8nGd97/3PNTPad2uxbMm75HjXYmeXbSALIYaQDRoCFEqBkkJxact5Sg8Fly7Q9pQetwXOQzl90QMPBQ44acC0tKRtLNkJia3Fju3EsR07lixZ+0gajaRZruePezQarZYtW6Pl+3699JJm7nvuuWYUOfrqd12/y1hrERERERERkfnHFe8BiIiIiIiIyPVRoBMREREREZmnFOhERERERETmKQU6ERERERGReUqBTkREREREZJ5SoBMREREREZmnFOhEROLIGOM2xvQZY1beyHNlcsaYpcaYV40xSXF47hpjzIdn+3lvFmNMozHmLZGv/9AY879uwnN8yxjzBzfgOo8aY757I8YkIjKXKNCJiFyDSKAa/ggbY/wxt99/rdez1oastenW2jdv5LkypT8AvmWtHYRoyBqIfA+9xpjnjTGbp3MhY4zHGGONMatv9CCNMfdGrr1/zP0vGmM+cKOfb6astX9srf3ETK5hjPmoMea/xlz3o9baP5vR4BxPA9un+70VEZkvFOhERK5BJFClW2vTgTeBd8Xc9/+NPd8Y45n9Uc4/s/U+GWNSgA8CY79Xn4h8T3OBw8A/zsZ4pqEX+IgxZsVML7TY/1u01lrg+8DH4j0WEZEbSYFOROQGMsb8iTHmB8aYfzLG9AIfMMbcGamqdBtjmo0xf2OMSYicP6rCY4z5buT4vxhjeo0xLxhj1lzruZHj7zDGnIlUnf7WGHN4sul+U40xcnyrMeYXxphOY0yLMea/xYzpD40x54wxPcaYo8aY5caYEmOMHfMc0emGkUrMocjzdAKfN8aUGmP+0xjTYYxpN8Z8xxiTFfP4VcaYZ4wxbZHj+40xyZExb4w5b5kxpt8YkzvBS70TaLXWNk/0Plhrgzi/9G+a5ntzKPL5ZKTC91jkMY8aY+oj78lZY8z9MU+zxhhzJPI9+1djzJKJxhLRCXwX+MJEB40xLmPMF4wxF40xrcaYbxtjMiPHSiL/vfyaMeZN4N9i7vuwcaZLdhpjPmaMud0YcyLyGvfHXH/K78mYsfyJMebbka//lxldzQ4aYz4fOfZ5Y8z5yOs/aYx5KHL/VuDvgJ2Rx7RH7v+uMWZfzPN8IvKedkT+e1gWuX/45+M3Ise7jDF/M2aY/wXsmeL9FhGZdxToRERuvEeA7wFZwA+AILAXyAPuBh4AfmOKxz8J/CGwBKcK+MfXeq4xpgD4IfDZyPO+Adw2xXUmHWPkF/hfAD8BlgHrcX4xJnL9xyPnZwMfBQameJ5YdwGngXzgzwED/EnkOTYBayOvbbi6dBA4C6wGVgA/tNYORF5n7BTEJ4GfW2s7JnjOrcBrkw3IGJMIvB94Mebuqb5/uyKfN0eqtD82xtwF/APwuzjvyVuBi2PG9yFgKZAG/M5k44n4E+AJY0zJBMc+ivPa3wKsA3KA/WPO2QVsYHSQ2RE5/wPA3wC/D7wN2ILzR4i7I+dN+j2ZirX2EzGV7N1AF/Bs5PAZnPcxC/hT4HvGmKXW2hPAp4DqyGPzxl43Eoy/hPPfXBFwmfHV1geB7UBF5LXcG3PsNFBijEm92msQEZkvFOhERG68GmvtT6y1YWut31r7srX2l9baoLX2PPBNnF9yJ/Mja+1Ra20A55fV8us4951AvbX2nyPH/hpon+wiVxnjQ8Ala+1+a+2gtbbHWvtS5NhHgT+w1r4eeb311trOqd+eqDettd+IrA30W2vPWGufs9YOWWtbI2MeHsOdOIHq/7HW+iLnH44c+0fgSWOMidz+IPCdSZ4zG2ca41hfN8Z0A33Ax3FCw3Tem4n8OvD3kdcSttZestbGhsj/HXm/+oH/y9TfX6y1TcDfA380weH3A//DWvuGtbYXZ33gk8aY2P+/f9Fa22+t9cfc98eR7+XPgCHgu9baNmttI1CDE4a4yvfkqowxS3HWrj1lrT0eueYPrbXNkffme8AFnIA5He/HWf9YHwnzvw/sNsYUx5zzZWut11p7AecPD7Hv7/D3Pnu6r0FEZK5b1PPpRURukkuxN4wxG4C/wqkapOL82/vLKR7fEvN1P5B+Hecujx2HtdYaYxonu8hVxrgCpzI2kRXAuSnGN5Wx71MhTrXobiAD54+ObTHPc8FaGxp7EWvtYWNMEKgyxnQBK3GqeRPpilx7rN+01n47EoR2Av9sjLnbWnvyOr5/K4CXpzh+Ld/fYV8Gzhpjtoy5fzmjq38XgUScquewUe8zgLX2SsxNPzD2djpc9XsypUi188fAt621P4q5/8PAZ4BVkbvSccL6dCwHjsS8jp7I97yIkfd1qvd3+HvfPc3nExGZ81ShExG58eyY2/8v8ApQYq3NxFkPZcY96sZqBqJVi0j1qmiK86ca4yWc6XkTmeyYL/K8sVPbCsecM/Z9+nNgENgaGcOHx4xhlTHGPck4/g/O9MEP4kzFHJzkvOM4U0YnFKkaPY8zRfW+yN1TvTdjX8PwWCd7v66LtbYN+FtiKocRlxkJRuCE2SFiQlekGcj1mup7cjVfw6kKf3H4DmPMWuAbwFNArrU2G3iVqd/PWKNerzEmA2eaadM0x7QROBupjoqILAgKdCIiN18G4AV8keYdU62fu1F+ClQaY94VWX+2l9FVm2sZ47PASmPMp4wxicaYTGPM8Hq8bwF/YoxZZxzlkSYfLZGPDxhn/7yPMzp4TDYGH+A1TlfH34s59gLQAfyZMSbVGJMSs84LnCmWj+OsT/s/UzzHC0B+pPI0och1NwAnY8Y14XsTqRh24KwtG/a/gY8aY94aaVpSbIy5ZaoXPk3/A2etXGnMff8E/I4xZnUk3Pwp8E/W2vANeD6Y+nsyKWPMJ3GmyX5wTKBMxwltbc5p5qM47/WwK0CxiWnIM8Y/Ab9ujNlmnH0Ev4yz5m7S6vMYu4F/mea5IiLzggKdiMjN97s4TTB6cao9P7jZTxiZUvcrwFdxAsc6oA6n2nJNY7TWenGqVY8BrThNLYbXUf0l8AzwHNCDs74sOfJL/Mdw1nS1AyVMPU0RnErObTjh6Vmc6XrDYwjirAvciFMBexMnwA0fvwCcAIastUeYRKRy9x2ctVixol0ZgW/jrNX796u9NzHj/l6kQ+Sjkef/GM5URS/wnzjTMGfEWtuNE+piu2L+fWQ81cD5yBj3zvS5Ykz6PbmK9+EEz+aYTpf/LbKO7m+Al3CqyBsY/d/FvwOvA1eMMS1jL2qt/VecKuXTkcevZPz3ckKRKvUTOP+NiogsGGZmMzFERGQ+iExVvAw8bq2tjvd4bgZjzP8Bzltr913lvKVEmmVMMTVTFhhjzCPAe6y1T8Z7LCIiN5ICnYjIAmWMeQBniuEA8DmcqtHahRhiImuz6nDWer0Z7/GIiIjMFk25FBFZuKpwpuG14+yd9vACDXNfBhqAP1OYExGRxUYVOhERERERkXlKFToREREREZF5as5tLJ6Xl2dXr14d72GIiIiIiIjExbFjx9qttVNtNxQ15wLd6tWrOXr0aLyHISIiIiIiEhfGmIvTPVdTLkVEREREROYpBToREREREZF5SoFORERERERknlKgExERERERmacU6EREREREROYpBToREREREZF5SoFOREREREQWlYPnD3L/j+5n2z9u4/4f3c/B8wfjPaTrNuf2oRMREREREblZDp4/yL4j+xgIDQDQ7Gtm35F9AOxZuyeOI7s+qtCJiIiIiMiisb92fzTMDRsIDbC/dn+cRjQzqtCJiIiIiMiCMhQaoqmvicbexujnxr5GGnsbafY1T/iYFl/LLI/yxlCgExERERGRecVaS8dAB429jVzqvRQNa8PhrbW/FYuNnp/kTqIovYjijGIu9V6iP9g/7pqFaYWz+RJuGAU6ERERERGZc/oD/TT1NY2rsA0Ht7HTJgtSCyhOL+b2ZbdTnF5McUbkI72Y3JRcXMZZbTZ2DR1AsjuZvZV7Z/X13SgKdCIiIiIiMutC4RBt/janwjZcXYsJbR0DHaPOT/WkUpxRzKrMVdxddDfFGcXRqltRehFJ7qRpPe9w45P9tftp8bVQmFbI3sq987IhCoCx1l79rFm0Y8cOe/To0XgPQ0REREREZqh3qHekwhZTZRuuvAXCgei5LuNiWdoyitOLKcooGqmyRT5nJ2VjjInjq5k9xphj1tod0zlXFToREREREbkugXCAFl/LhM1HGvsa8Q56R52fmZhJcUYx63PW87aVb4tW11akr6AwvZAEV0KcXsn8pUAnIiIiIiITstbiHfSOCmmxn1t8LYRsKHq+x+VxpkGmF7M5d/OodWxFGUVkJmbG8dUsTAp0IiIiIiLz3MHzB697Tdhwi//Jpkb2BfpGnZ+bnEtRRhFl+WXsWbtn1NTIgtQC3C73zXiJMgkFOhERERGReWxs18ZmXzP7juwDnAYgY1v8j50aOVGL/+GQtqNwR7TiNjw9MjUhNR4vUyahpigiIiIiIvNI2IYZCA4wEBpgIDjAB372Adr8bePOS3InsSJjBY29jeNa/C9NXRrtEDlcXVuRsYKi9CLyUvIWTfORuUpNUURERERkXpvJFMJ4sdYyGBqMhi1/0D/u6+Hj/qA/GsgmvB3yR78eda3gAEPhoWmNZzA06LT4X373qK6Ry9OXT7vFv8x9CnQiIiIiMqdcbQrhtbLWEggHph+gJglUg8HB6GPGPn74nOuR7E4m2RP5cCeT4kkh2ZNMqieVJclLSPZE7nMnk+RJIsWdMur8rx77Kt2D3eOuuyxtGf/zrf/zusYk84cCnYiIiIjMKftr94+bIjgQGuDPfvlnNPU1TSuQja2QhW34mseR4EpwwlRsgIrczkjNiAaqaOAae9udPOq+2McPf53kTsJlXDN6vxLdiaMCMDghcW/l3hldV+YHBToRERERmRMC4QD1rfU0+5onPN4z1MPf1v0tbuOeMEAluZPISc4ZHabGVL3G3Z7kWJI7ad50axyuWs63KapyY8wo0BljHgD2A27gW9bar4w5vhL4RyA7cs7vW2t/NpPnFBEREZGFo2ugi5qmGg41HuJw02F6A72Tnrs0dSn/8ti/aPPpCexZu0cBbpG67kBnjHEDXwPuAxqBl40xz1prT8Wc9nngh9babxhjNgE/A1bPYLwiIiIiMo9ZaznTdYZDjYd4vvF5jrcdx2LJS8nj3lX3srt4N92D3Xzlpa+Mm0L4me2fUZgTGWMmFbrbgLPW2vMAxpjvA+8GYgOdBYa3g88CLs/g+URERERkHvIH/bzU/FI0xF3pvwLA5tzNPFX2FLuKd7Exd+OotWTJnmRNIRSZhpkEuiLgUsztRuD2MefsA/7NGPNbQBpw70QXMsZ8HPg4wMqVK2cwJBERERGZC5r7mqMB7qWWlxgMDZLqSeXO5XfyyeJPUlVURX5q/qSP1xRCkemZSaCbaLfBsbuUvw/4trX2r4wxdwLfMcZssXZ0myFr7TeBb4KzsfgMxiQiIiIicRAKhzjefjwa4l7veh2A4vRi3rP+Pews3smOpTtIdCfGeaQiC8tMAl0jsCLmdjHjp1T+OvAAgLX2BWNMMpAHtM7geUVERERkDvAOejly+QjPNz7P4abDdA924zEeKpZW8Hs7fo+dxTtZk7kGYyaqA4jIjTCTQPcyUGqMWQM0AU8AT445503gHuDbxpiNQDLQNoPnFBEREZE4sdZy3ns+WoWrb60nZEPkJOWwq3gXO4t3ctfyu8hMzLz6xUTkhrjuQGetDRpjPgX8HGdLgn+w1p40xnwJOGqtfRb4XeDvjTGfwZmO+WFrraZUioiIiMwTg6FBjrYc5fnG5znUeIimviYAbsm5hY9s+Qi7V+xmS+6WebNnm8hCY+ZavtqxY4c9evRovIchIiIismi19rdS3VjN843P82Lzi/iDfpLdydyx7A52Fu9kV/EuCtMK4z1MkQXLGHPMWrtjOufOaGNxEREREZn/wjbMyfaT0Src6c7TACxLW8ZD6x5iV/Eubiu8jWRPcpxHKiJjKdCJiIiILEJ9Q3280PwCz196nuqmajoHOnEZF+X55eyt3Mvu4t2UZJeooYnIHKdAJyIiIrJIXOy5yPOXnudQ0yGOXTlGMBwkIzGDqqIqdhXvomp5FdnJ2fEepohcAwU6ERERkQUqEApQ21rL843PU91YzYWeCwCUZJfwwU0fZHfxbsryy/C49CuhyHyln14RERGRBaTD30F1UzWHGg9x5PIRfAEfia5Ebl12K09ufJJdxbsoSi+K9zBF5AZRoBMRERGZx6y1vNr5arShySvtr2CxFKQU8MDqB9hdvJvbl91OakJqvIcqIjeBAp2IiIjIPNMf6OfF5hc51HiI6sZqWv2tGAxb87bym+W/ye7i3WxYskENTUQWAQU6ERERkXmgsbeRQ42HONR0iJebX2YoPER6Qjp3Lr+T3cW7qSqqIjclN97DFJFZpkAnIiIiMgcFw0HqW+s51HSIQ5cOcc57DoDVmat5YsMT7CreRWVBJQnuhDiPVETiSYFOREREZI7oHuim5nINhxoPcbjpMD1DPXhcHrYv3c5j6x9jV/EuVmWuivcwRWQOUaATERERmQUHzx9kf+1+WnwtFKYVsrdyLw+ueZDXu193plI2HqKhrYGwDbMkeQlvXfFWdq/YzZ3L7iQ9MT3ewxeROcpYa+M9hlF27Nhhjx49Gu9hiIiIiNwwB88fZN+RfQyEBqL3uY2b9IR0vENeADYu2cjuFbvZVbSLzXmbcRlXvIYrInFmjDlmrd0xnXNVoRMRERG5iQaCA/zly385KswBhGyIgdAA++7cx87inRSkFsRphCIynynQiYiIiNxA3kEvda111F6p5VjrMU51nCIYDk547lBoiMfWPzbLIxSRhUSBTkRERGQGmvuaOdZ6jLorddS21nK2+ywACa4EtuRt4Vc3/SpPn32aroGucY8tTCuc7eGKyAKjQCciIiIyTWEb5lz3Oepa6zh25Ri1rbW0+FoASE9Ip6ygjAfXPEhFQQVb8raQ7EkGYH3O+nFr6JLdyeyt3BuX1yEiC4cCnYiIiMgkAqEAJztOUttaS92VOura6vAOOk1M8lLyqCyo5MObP8z2pdspzS7F7XJPeJ09a/cAjOtyOXy/iMj1UqATERERifAFfDS0NnCs9Ri1V2o50X6CwdAg4Gzofc/Ke6goqGB7wXaKM4oxxkz72nvW7lGAE5EbToFOREREFq12fzu1V2qjUyhf63qNsA3jNm42LNnAe9a/h+1Lt1NeUE5eSl68hysiMo4CnYiIiCwK1lou9V6Krn2ra63jYs9FwFnPti1/Gx/b+jEql1ZSll9GWkJanEcsInJ1Mwp0xpgHgP2AG/iWtfYrY47/NfDWyM1UoMBamz2T5xQRERGZjlA4xGtdr0Wrb3WtdbT72wHISsqioqCCx0sfp3JpJRuXbCTBnRDnEYuIXLvrDnTGGDfwNeA+oBF42RjzrLX21PA51trPxJz/W0DFDMYqIiIiMqmB4AAn2k9Ep1DWt9XjC/gAWJ62nDuW3eGsf1u6nTVZa3AZV5xHLCIyczOp0N0GnLXWngcwxnwfeDdwapLz3wd8cQbPJyIiIhLlHfRS31ofbWBysuNkdAPvkuwS3rn2nVQWVFK5tFL7vYnIgjWTQFcEXIq53QjcPtGJxphVwBrgPyY5/nHg4wArV66cwZBERERkoWrxtUSnTh67ciy6gbfH5WFL7hY+uOmDbC9wGphkJWXFebQiIrNjJoFuoj69dpJznwB+ZK0NTXTQWvtN4JsAO3bsmOwaIiIiskiEbZg3vG+MNDC5Usdl32UA0hLSKM8v5x1r3kFFQQVb87ZGN/AWEVlsZhLoGoEVMbeLgcuTnPsE8MkZPJeIiIgsYIFQgFOdp6i9UhvtQDm8gXduci6VSyv51c2/SmVBJaU5pXhcatQtIgIzC3QvA6XGmDVAE05oe3LsScaYW4Ac4IUZPJeIiIgsIP2Bfurb6qMNTI63HWcgNADAqsxVvG3F26INTFZkrLimDbxFRBaT6w501tqgMeZTwM9xti34B2vtSWPMl4Cj1tpnI6e+D/i+tVZTKUVERBapdn87da110Qrca52vEbIhXMbFhiUbeHy9s31ARUGFNvAWEbkGZq7lrB07dtijR4/GexgiIiJyFQfPH2R/7X5afC0UphWyt3Ive9buwVpLY29jtPtkXWsdF3ouAJDkTmJb/jan+2RBJWUF2sBbRGQsY8wxa+2OaZ2rQCciIiLX6uD5g+w7si86TRKcbpMbcjZwpf8Kbf42ADITM6NbB1QurWTTkk3awFtE5CquJdBpRbGIiIhMW3+gn7PdZ/nyL788KswBBMNBTnWe4h1r3hGtwK3NXqsNvEVEbiIFOhERERknEA5w0XuR17tf5/Wu16Ofm/qapnyctZav7PzKLI1SROQ6Hf8hPPcl8DZCVjHc8wXY9t54j+q6KNCJiIgsYtZaWnwtvN79Ome6zvB61+uc7T7LG943CIQDALiNm9WZq9mat5VHSh6hNKeUP33xT2n1t467XmFa4Wy/BBGRa3P8h/CTTxPoGaTpyBKK72rC85NPO8fmYahToBMREVkkvINeznSd4Wz3WafqFglvfYG+6DmFaYWUZpdSVVRFaU4ppdmlrMlaQ6I7cdS1/EH/uDV0ye5k9lbunbXXIyJyzcIh+MU+CPhpP5mJvy2RtpPpLNvR41TsFOhEREQk3gaCA5z3no+Gtte7X+ds19lRFbXMxExKc0rZs3YP63PWU5pTSkl2CRmJGdN6jj1r9wBM2OVSROSmCw6Cvwv83ZHPV/kYiJw34MVa8F1JpPtcGmDwvpFG/uY+PDTG+1VdFwU6ERGReSoUDnGp91I0sA2vc3uz903CNgxAoiuRddnruGP5HZRml1KSU0JpdikFqQUz3qx7z9o9CnAicv2shSHf1UPYRMEt0D/5dY0LUnJGPtILCKatxXdxCN8FL30NZwn5R/79s2GcKt09WbPwom88BToREZE5zlpLm78tGtqGp02e7z4fnfJoMKzMXElJdgkPrHmA0uxSSnNKWZGxAo9L/7sXkZsoHIIB75gQdrXKWeR4ZK3uhNxJo4NZ9ipYVg4p2aPvH3s7MQMbDuM/fgJfTTV9/3aYgRO/BGtxZ2WRsnkDfcdeBRsJdTZSpdv+mXkZjubjmEVERBasvqE+znafHb3Wrft1vIPe6Dl5KXmUZpfy3lveS0l2Cetz1rM2ey0pnpQ4jlxE4upGdG0MDo2pik0SwsZV0rzAFHtbJ2aMDl4FmyYIZRN8JFzbv2mBlhZ8z/07fdU1+F54gXBPD7hcpGzbRt6nPkl6VRXJW7bQ8sd/AvVnIRiKPtYaD23/2cSyXdf2ls0FCnQiIiJxEAgFeKPnjXHr3C77LkfPSfWkUpJTwr0r76U0p5T1OespyS4hJzknjiMXkTkn0rWRgN+57b0Ez/6W87n4tqtXyoZD3FDf5M9hXJCcPRLEUpdA7rqJg1hy9ujqmTvhprzs8OAg/UeP4qs5jK+mmsHXzwLgKSgg4757Sd+5k7Q77sCdnT3qcf76+lFhDoBgCH9d3U0Z581mrJ0iTcfBjh077NGjR+M9DBERkRsibMNc7rsc7Sg5HN4ueC8QtEEAPMbD6qzVo0JbaU4py9OWz3idm4gsYOEQtL0G337QCWTT4UpwwtikIWySyllSJrhcN/f1XIW1lqELF/BV19BXU03/Sy9jBwYwCQmk3rqDtLurSNtZRVJp6bz/t9MYc8xau2M656pCJyIicoN0DnSOak4yHOL6gyOL95enLac0p5S3rHhLdJ3b6szVJNykv2CLyAJhLXS/CZdroekYNNXC5XoI+KZ+3Id+OjqkJaTCPAo7ob4++l98kb6aGnzVNQSamgBIXLWK7McfJ31nFam33oorNTXOI40fBToREZGIg+cPTqsNvz/o51z3uWi1bTi8dQx0RM/JTsqmNKeUh0sejnaWLMkuIT0xfTZfkojMV76O0eGt6Rj0tzvH3EmwbBtUfACKtsMvvgC9LeOvkbUC1uyc3XHPkA2HGXz1VWcdXE0N/XV1EAziSk0l9Y47yP3or5NWVUXiihXxHuqcoUAnIiKCE+ZiN8pu9jWz78g+Wn2tLM9YPqridqn3EjbSACDJncS67HUjG3FHNuPOS8mb91N+RGSWDPmguWEkuDUdg+6LkYMG8jfA+gegqNL5KNgMnsSRxxszeg0dOA1F7vnCrL6M6xXs7MR3+IjTkbLmMKEO549jSRs3kvtrv0ZaVRWpFeWYxMSrXGlx0ho6ERER4P4f3U+zr3nS4y7jYmXGymhgGw5vxenFuF3uWRypiMxroQC0nnZC2+VaJ8S1nnI2QwPIWglFFU7lrWg7LCuDpIyrX/dGdLmcJTYYxN/QQF91Nb6awwycPOlsKZCdTdrdd5O2s4r0u+/Gk58f76HGjdbQiYiITMNgaJCjLUepaaqZMsz94J0/YG3WWpI9ybM4OhGZ96yFzvNwuW6k8tZ8HIKRSlpKjhPaNuyB5ZHqW3rB9T3XtvfO2QAHELh8OboOzvfii4R7e50tBcrLyf/0b5FWVUXypk0Yt/5Adq0U6EREZFFp7G2kpqmGmqYaXmp5CX/QT5I7iSR3EoOhwXHnL0tbxqbcTXEYqYjMO71XxjQtqR3pPulJgeXlsOMjkamT2yFn9bxqUHItwgMD9L98NDqNcujcOQA8hYVkPvB20qp2knbnHbgzM+M80vlPgU5ERBa0odAQta21VDdWU91UzRveNwAoTi/m4ZKH2Vm0k1sLb+W5N58btYYOINmdzN7KvfEauojMZYO9TpfJ4cpbUy30NDrHjNvZPHvjQyPhLX8juBfur97WWobeeANfdTV91TX0v/wydnAQk5hI6q23RjtSJq5bp/XFN9jC/a9KREQWrea+ZqqbqqlpquHF5hfxB/0kuBK4tfBW3rP+Pews2smqzFWjfqkY7mY5nS6XIrI/CnK2AAAgAElEQVTIBIfgyiuRdW+R6ZNtr0GkORI5a2Dl7VD0m054K9wGiQu/jX6otxffCy/gqzlMX001wcvO1PXENWvI/pX3kl4V2VIgJSXOI13Y1BRFRETmvUA4QH1rfbQKd7b7LODs+bazeGe0CpeasPB/wRKRGQqHofNcTOXtGLScgNCQczwtf6RhyfC6t9Ql8R3zLLHhMAOnTjvTKKtr8NfXQyiEKy2N1DvvIL1qp7OlQHFRvIc676kpioiILHhXfFeia+FeaH4BX8CHx+Vh+9Lt0amUa7LWaGqPiEyt5/Lovd4u18Fgj3MsMR2WlcPtnxgJcVnFC3bd20SCHR34Dh929oU7fJhQZycAyZs2kfvRj5JedTcp5eWYhIQ4j3TxmlGgM8Y8AOwH3MC3rLVfmeCc9wL7cGrSDdbaJ2fynCIisjgFw0Ea2hqobnSmUr7W9RoAS1OX8o4176CqqIo7lt1BWkJanEcqInOWvzum42SkaUlvpMOtywNLt8DW94yse8tbD4tsWxIbCOCvr49u7D1w6hQA7iVLSLv7btJ3VpF211148vLiPFIZdt2BzhjjBr4G3Ac0Ai8bY5611p6KOacU+Bxwt7W2yxhznX1YRURkMWr3t1PTVEN1YzUvXH6B3kAvHuOhYmkFn9n+GXYW7aQku0RVOBEZLzDgTJWM7vd2DDrOjhzPLYE1u0Yqb0u3QMLi3JpkqLEJX00NfTXV9L/wImGfD9xuUirKyf/tvaRV7SR500aMyxXvocoEZlKhuw04a609D2CM+T7wbuBUzDkfA75mre0CsNa2zuD5RERkgQuFQ5xoP8GhxkPUNNVwuvM0APkp+dy3+r5oFS4jcRqb7IrI4hEOQfuZ0R0nr7wC4aBzPL0QindA2fsia98qICU7vmOOo7DfT//LL0ercENvON1/PcuXkblnD2k7q0i74w7cGfq3dj6YSaArAi7F3G4Ebh9zznoAY8xhnGmZ+6y1/zr2QsaYjwMfB1i5cuUMhiQiIvNNh7+DI5ePUN1YzZHmI3gHvbiNm7L8MvZW7mVn0U7W56xXFU5ksTn+Q3juS+BtdNat3fMFZ+Nsa8F7KWbdWy0018NQn/O4pEwnsN316Uj1rRIyl8f3tcySQGsrTb/zuxT/9Vfx5OdH77fWMnT2LH01h/FVV9N/9Ch2aAiTlETqrbeS88SvkLZzJ4lrtO54PppJoJvouz22ZaYHKAXeAhQD1caYLdba7lEPsvabwDfB6XI5gzGJiMgcFwqHONlxMjqV8mTHSSyW3ORc3lL8FqqKq7hz2Z1kJWXFe6giEi/Hfwg/+TQE/M5t7yV45ik4/DfQ1wK+Nud+d6KzRUD5kyNTJ5esg0U6NbD969/Af+wYbV//BgWf+W18R17Ad7iGvuoagi0tACSuW0fO+95HWlUVqbfuwJW8OKeZLiQzCXSNwIqY28XA5QnOedFaGwDeMMa8hhPwXp7B84qIyDzTPdDN4cuHqW6q5kjTEboGu3AZF1vztvLJ8k9SVVzFxiUbcZnF+UuYyKJhLQT6wdcO/e3g64h8jr3dAeeeG9kmYFg4CG2nYduvOFW35ZXOujdPYnxeyxxgrSXs8xFsa2Pw7Fm6f/QjsJbu73+f7h/8AMJhXOnppN15J2m/+RTpVVUkLF8c1crFZCaB7mWg1BizBmgCngDGdrB8Bngf8G1jTB7OFMzzM3hOERGZB8I2zOmO01Q3OfvCnWg7gcWSk5RDVVEVVUVV3LX8LrKTF+8aFpEFwVoY6B4TzDqmCGvtEByY+FquBEjLg9S88WFuWDgED3/95r2eOSI8OEiovZ3g8Efb8NdthDo6Ym63YwcmeD+tJWnTJgr/4HOkbNumLQUWuOsOdNbaoDHmU8DPcdbH/YO19qQx5kvAUWvts5Fj9xtjTgEh4LPW2o4bMXAREZlbvINeXrj8AtVNzrYCnQOdGAxb8rbwVNlT7CzeyabcTarCicxl4RD0d44JYpGQNny7v2MknPV3jDQeGSshDdJyITUX0gqgYFPk60hoi36OnJOUObK/219vcaZZjpVVfPNe+01mg0GCnZ1OIBsb0mJvd3QQ7umZ8BrunBw8ebm48/JIqajAk5eHJy8Pk5TIlT//CwgEoucOnT1L4sqVCnOLwIz2obPW/gz42Zj7vhDztQV+J/IhIiILiLWW17peo7rRqcI1tDUQtmGykrK4a/ld7Czayd1Fd7MkeUm8hyqyeAUHJw5ik4U1fzfjWyJEJGeNBLGc1VC83bk9KqTljpyTkHL9477nC6PX0IFzvXu+MPlj4sBaS9jrHV1J62gfHdAiH6HOTqeiOYYrLQ1PXh7u/DySbrmFtLw8PHm5zn15eXjy8vHk5+FZsmTScNa874/Gjy0cpu3r32DZF+fWeyY33owCnYiILC69Q728cPkFappqqGmqoc3vNCbYlLuJj239GFVFVWzN24p7kW3EKzItk3VtnC5rYcg3yXTG9gkqax0w1DvxtYwLUpaMBLGlmyaomsWGtVxwz2KlZ/h9mcn7NQPh/v4x0x3bnFDW3k6wvWNUUIutig0ziYm483Lx5OWTUFRESlmZU03LHw5pkY/cXFypqTMer7++fvw4AgH8dXUzvrbMfcZO8JeCeNqxY4c9evRovIchIiI4f30+03XG6UjZVE1DawNBGyQjMWNUFS4vJS/eQxWZ28Z2bQSn4vT2P4PVO0dXycZWzaLr0jomX3/mThxfIUvNneC+yOfk7HnRCXKyNvzXww4NEezoiASymIA2tpLW3k64v3/8BVwu3LlLnIpZNJRNUEnLy8OVkaH2/zIjxphj1tod0zlXFToRERnFF/Dx4uUXow1NWvtbAdiwZAO/tuXXqCqqYlv+Njwu/S9EZJxwGPyd0Hcl8tHqfDz/F6PDHDi3f/qZia8TXX+WBxmFTjfHqcJaUsbI+rMFJLYN/0RTB204TKira1QlbWzTkGB7G6G2dkJe74TP4c7KilbNUrZuHVNJyx8JbTk5GLdmH8jco/8bi4gsctZaznvPU93oNDM51nqMYDhIekI6dy6/M1qFK0gtiPdQReLDWhjsgb62MUEt5rOvdSS82dC1Xf+Rb44PazNZf7ZABFpb8R444LTh/7//F+NyOVMhOyKVtLZ2gp2dEBr/fpuUFDz5TiUtae06PLfdHpkCObqS5s7NxZW4eLc9kIVBgU5EZAE7eP4g+2v30+JroTCtkL2Ve9mzdg/9gX5eankp2tCk2dcMQGlOKb+66VepKqqivKCcBJe6o8kCFvCPhLBRoSw2sEW+nmiqo8sDafmQXgDpS6Fwq/M5fenIfWkFztffuGuSro0roOxXbv5rnUfCQ0P0/cd/cOXP/wI7FNm+IBik63vfw1NYiCc3l4SCpaRs3hyzHi0S0nKd0OZKS4vvixCZRVpDJyKyQB08f5B9R/YxEBr5RdTj8rA6YzUXey8SCAdI9aRyx7I72Fm8k6qiKgrTCuM4YpEbIBR01p1NFMr6rsRU2VphcOIpeKTmjgll+eODWvpSSMmZ/jq0ydbQvetvZq3Rx1w3cPo03T8+QM9PfjLh9EiTlETJL/59xmvpROYDraETEVnkrLX81dG/GhXmAILhIBd6LvD+je9nZ/FOKgsqSZjNznUi18Na8HfFhLMppj72dzBh2/3EjJEwtnQzrHtbTDgrGB3ebsbPRJy7Ns5Vwa4uen56kO4DBxg8fRqTkEDGffcS8vXjO3JkVOdGteEXmZgCnYjIAuAL+DjRfoKG1gaOtx/neNtxuge7Jzw3ZEP83q2/N8sjlAVppm34B/smDmV9V8AXU0nra4Xw+NbwuJNGAlnOKlhx6/gqWnqBM+0xceat4Wds23sXfYADsKEQvsOH6T7wNH3PPYcNBEjevJmlf/h5svbswZ2dzfmHH1EbfpFpUqATEZlnwjbMhZ4LHG87TkNbAw1tDZztOouNVCXWZa3jrSveyn+8+R94h8ZPW9K0Srkhxk4h9F5yboeDThv+vtbImrQppj4GfOOva1yj16UVbJp86mNy1oLs7LhQDV24QPeBp/E+8wzB1lbc2dnkPPk+sh59lORbbhl17tpnno7TKEXmHwU6EZE5rneo16m+tTVwvM2pvvUM9QCQkZDBtvxt3LfyPrblb2Nr/lYyEzMBuH3Z7ePW0CW7k9lbuTcur0MWkMAA/Nt/n7gN/zNPTfyYlJyRUFa0Y0wlLeZzai5oY/oFI9Tno/fn/0r3gafxHzsGLhfpO3eS9fn/TsZb3oJRh0mRGVOgExGZQ8I2zBveN0ZV3851n8NiMRjWZa/jvlX3UZZfRll+GauzVuMyEzdl2LN2D8CEXS5Fpi3gh5ZXoLne+bjcAG2nnUrcZN61f3xTEU/S7I1Z4spai//YMafByc9/ju3vJ3HNGvJ/93fIeujdJCzVFigiN5K6XIqIxFHPUA8n2mKqb+3H6R3qBSAzMZNt+dsoyy9zqm95W8lIzIjziGVBG/KNhLfL9dDcAG2vjuyrlrIElpfDsnKo/cdIA5IxslbAZ16Z3XHLnBBoacH7zDN0P/00gYtv4kpLI/PBd5D1yKOkVJRjND1WZNrU5VJEZA4K2zDnus+Nqr6d954HwGAoySnh7avfHq2+rcpcNWn1TWTGBvug5bgT2i5Hqm/tZ8CGneNp+U5w2/AgLCtzvs4qHlmzVrBx4jb896gD4WISHhqi77nn6P7xAacrZThM6m23kffUU2Tefz+u1DnQjEZkgVOgExG5SbyD3mh4O952nBPtJ+gL9AGQnZTNtvxtPLjmQcoKytiat5W0BG2EKzfJQI8T3oaDW3MDtL9OtL1/eqET2ja92wluy8shY9nUDUfUhn/RstYycOoU3gNP4/3pTwl7vXiWLSPvE79B1sMPk7hyZbyHKLKoKNCJiNwAoXCIs91nOd5+nIZWp/p2oecCAC7jojS7NBreyvLLWJmxUtOP5Obwd48Ob5frofPcyPGM5U5g2/K4E+KWl0PGdXY+VRv+RSXY1UXPT35C94GnGXz1VUxiIhn33kvWY4+SdscdGLea2YjEgwKdiMh16B7o5nj7cepb6znefpxX2l/BF2nBnpOUQ1l+GQ+te4iy/DK25G0hNUHTjuQm6O90qm3DVbfL9dD1xsjxrBVOaCt7X2TtW5nTqERkmmwwSF9NDd4DT9P7n/8JgQDJW7aw9At/6OwZl5UV7yGKLHoKdCIiVxEMB53qW8zat4s9FwFwGzfrc9bzzrXvjK59W5GxQtU3ufF8HTGdJiOfu98cOZ690pkuWfGBkcYlaXnxG6/Ma4Pn38D79AG8z/wzwbY23EuWsOTJJyN7xq2P9/BEJIYCnYjIGJ0DnePWvvmDTuOHJclL2Ja/jYdLHqYsv4zNuZtVfZMbr69tdHBrbnA27h6WsxqWV8KOjzjBbVkZpC6J23BlYQj1+ej913+h+8cH8NfVgdtN+q5dZD36CBm7d2vPOJE5SoFORBa1YDjIma4zo6pvl3qdX5w9xsP6Jet5uOTh6PYBxenFqr7JjdV7ZXx462kaOb5kHay4DW77WCS8bXM26Ra5Aay19L/8Mt4DTzt7xvn9JK5dS8Fnf4+shx7Ck58f7yGKyFUo0InIotLh74gGt+NtxznZcTJafctNzqUsv4zH1z9OWX4Zm3I3keJJifOIZcGwFnqbR/Z3Gw5xfS2REwzklsCqu0Y6TRZuhWStUZIbL9DcHNkz7hkCbzp7xmW9611kP/oIyWVl+sOVyDwyo0BnjHkA2A+4gW9Za78y5viHgb8Ehv/U+HfW2m/N5DlFRKYrEA5wpvNMNMA1tDXQ1Of8c+QxHjYs2cCjpY+yLW8bZQVlLE9brl9i5Maw1qmyxVbdLteDr9U5blyQtx7W7h4d3pK0cbzcPOHBQXp/8Qu8B5529oyzltTbbyf/U58k4777cKXoD1gi89F1BzpjjBv4GnAf0Ai8bIx51lp7asypP7DWfmoGYxQRiTp4/iD7a/fT4muhMK2QvZV72bN2DwDt/vbolgENbQ2c6jjFQGgAgPyUfMryy3jilicoKyhj45KNJHuS4/lSZKGw1mlOElt1a26A/nbnuHFB/gYouXek02ThVkhcGPsOBlpbafqd36X4r7+q6XlzkLWWgZOn8B74Md6fHiTc04Nn+TLynnqKrEcfIbG4ON5DFJEZmkmF7jbgrLX2PIAx5vvAu4GxgU5E5IY4eP4g+47si4a0Zl8znz/8eb53+nu0+9u57LsMgMflYdOSTdGpk2X5ZRSmFar6Jld3/IdTb5RtLXRdGF11a24Af6dz3OWB/I2w/oGRTpNLN0Piwm2c0/Z3f4f/2DHavv4Nln3xC/EejkQEOzvxPvss3gNPM3jmDCYpiYz77iP70UdIveMOjMsV7yGKyA1irLXX90BjHgcesNZ+NHL7g8DtsdW4yJTLLwNtwBngM9baSxNc6+PAxwFWrly5/eLFi9c1JhFZuDoHOnn4mYfpGuwad8xlXNyz8p5oeNuYu5Ekd1IcRinz2vEfwk8+DQH/yH2eZKj8IHhSRkLcgNc55kqAgo0jwW04vCUs7MpvsLMTf10d/bW19L/0MgMnTkSPJd1yC4mrV5NQXERicTEJxcUkFBWTULQcV5J+Jm82GwzSV12N98ABev/zvyAYJHnbNrIffYTMBx/EnZkZ7yGKyDQZY45Za3dM69wZBLr3AG8fE+hus9b+Vsw5uUCftXbQGPMJ4L3W2rdNdd0dO3bYo0ePXteYRGRhCNswF7wXqGuto661joa2Bi70XJj0fIPh+IeOz94AZWEZ8ELXRfjOw9DfMfE57kQnrC0rG1nzVrAJPAs7pNhwmKHz5+mvq8NfW4e/tpah4T+6JiTgzswk1NUF4TC4XHgKCnAlJRG4fBkbCIy6lic/3wl4xcUjga+oyLldWIjxqE/b9Ro8fx7vgQN0//M/E2prx71kCVnvfjfZjz5CUmlpvIcnItfhWgLdTP71bARWxNwuBi7HnmCtjf0/498Dfz6D5xORBcof9HOy/ST1bfXRAOcddKog2UnZlOeX83DJw3zn1HfoGBj/C3dhWuFsD1nmk4DfWePWdRG6LzpTJrsvjtw30H2VCxj4XBN4Fv4eXGG/H/+JE054q6ujv76esNf5WXTn5JBSUUH2ex4npaICz9KlnN/zTifMAYTDhLq6WPOLf8edm0uwtZVAYyOBpiaGGhsJNDYRaGyk/9hRggcPjjwOwO0mobAwGvYSikZX+Dz5eZoiOEaor4+en/0M74Gn8dfXO3vG7d5N9mOPkr5rFyYhId5DFJFZMpNA9zJQaoxZg9PF8gngydgTjDHLrLXNkZsPAadn8HwiskC09bdR11pHfVs99a31nO44TdAGAViTtYa3rXgbFQUVlBeUszpzdXTtW2Fa4ag1dADJ7mT2Vu6Ny+uQOSIUcDbd7oqEtO6LMeHt4khnyWGeZMheCdmroGgH5Kxyvv6X/wZ9V8ZfP6t4wYa5wJVW/HV1+Otq6a+tY+D0aQg6P4uJ69aRcd+9pFZUklJZQeLq1aPWoTbv+yNsbCjDqegNr6VLKCwkobAQdoz/A7MNBAi0tBBobBwV9gJNTfQdOkSorX3U+SYx0anmFRWNn85ZXIQ7O3tRrJG14TD9L72M9+kD9Pz837ADAySWrKPgs58l66F3qSmNyCJ13YHOWhs0xnwK+DnOtgX/YK09aYz5EnDUWvss8GljzENAEOgEPnwDxiwi80goHOJs91nqW+upa6ujvrU+unVAkjuJzbmb+dDmD1FeUE5Zfhk5yZNvmDzczXKyLpeyQIVD0NsyPqgNh7eeJrAxwcK4nRCWswrWvz0S2FY7IS5nFaQVwETVntDQ+DV0CSlOY5QFwIZCDL7+Ov21tfjr6vHX1hJocn4WTVISKVu3kvuRj5BSUU5KeTmenKk3L/fX18OYaZUEAvjr6q46FpOQQOKKFSSuWMFEvT7DAwMEmpyQN9TUNBL4GhvxnzgRrRoOc6WljUzfHDuds6gYd/r87igaaGqi+5ln8D79DIHGRlzp6dEplcnbti2KMCsik7vuNXQ3i9bQicxvvoCP423Ho9W3423H6Qv0Ac7G3cOVt4qCCjYu2UiCW9OCFj1rwdcemQY5NrRdhO5LEI4NDgYylkWCWqTSNlxly1kFGcvBfZ1/r7xal8t5JNTnY+B4A/2RtW/+hgbCPh8A7vy8aOUttbKS5A0bMInzpwoZ6u0dCXzDFb6YAGj7+0ed787OjoS7CSp8c7RhS3hggN5fPIf3wI/xvfCis2fcnXeQ/eijZNx7r/aME1ngZqUpys2iQCcyf1hrafG1jGpe8lrXa4RtGIOhJKeEinwnwJUXlFOcXqy/JC9Ww41HRgW1N0cqbQHf6PNTc8cHteyVkUrbigXfjORaWWsJXr7shLe6Wvrr6hl87TVnnZoxJK1f74S3igpSKitJKCpasD+L1lpCXV3Rit7YCt9cbthirWXglVfoPnCAnp8eJNzbS8Ly5WQ9+ihZDz9MYnHRrIxDROJPgU5EbopAOMCZzjPR5iV1rXW09jvrk1I8KWzL2xatvm3N30pmolpkLxpD/ZEpkG+ObjwyHN4GRk+RIynTCWrD0yBjw1v2SkhKj8vLmC9sIMDAq69Gtg9wKnDBVudn0aSmklK2LVKBqySlbBvujIw4j3jusOEwwba2kcA3psIXaGm5qQ1bJtqIPdjRgffZn+A98GMGXz/r7Bl3//1kP/YoqbfdpoYwIovQbHW5FJEFrmeoh4bWhmj17UT7CfxBZ31RYVoh2wu2U1ZQRkVBBetz1uNx6Z+UBWtU45GL4xuQTNV4ZMVt48NbSg4s0ArRzRDyevHX10cqcHX4T5zA+p2fRc/yZaTeeispFRWkVlaQtH69tgCYgnG5SFi6lISlS2H79nHHYxu2jO3QOWnDluXLR1f3pmjY0v71bzgbsf/d10jfvYvuAwfo+6/nnT3jyrZRuG8fmXseVAgXkWlThU5EAGeqT2NvI3VtTuWtvrWec93nsFjcxs36nPVUFFRE18Bpq4B5YrprwsIh6G2evFNk7+XRjUdcHud6o4La6pHAll6gwHadrLUELl6kP9K4pL+ulqGz55yDbjfJGzaQUllJamUFKRUVTidJmTXhgQECly9P2KEz0NhIaGzDltTU6HRO95IcvM/8c7STKIA7L4+shx5y9owrKZntlyMic5SmXIrIVQ2FhjjVcYqGtoZogBve4y0jIYNtBdsoz49Mn8zbSmpCapxHLNfs+A/Hd210J0HlByFz+ejwNmXjkVXjG5DMpPGIjBIeGmLglZORtW91+OvqCXU4P4uujAxSKsqdtW8VlaRs24orVT+Lc1mor29kvV5TE0Mx6/cGz5+HUMg50RjSdlax4mtf055xIjKOAp2IjNM10BXdOqChtYFX2l9hKDwEQHF6cbTyVl5QTkl2CS6jNRvzkr8L2s9Cx+vOvmqDvZOfm5o3SafI1ZG919R45GYIdnZG1r7V4q+tY+CVV6JNOhJWrow2LkmpKCeppETrpxaIQGsr5+67Hzs4GL3PJCVR8ot/1/5xIjKO1tCJLHLWWt7wvhFtXlLfWs+FngsAeFweNuVu4okNT0RDXF5KXnwHLNcmOARdb0DHWWh/3Qlv7Wed2/3tV388Bj7XqMYjs8CGwwydPz9q77ehixedgwkJpGzeTM4HPhDtQOnJ08/iQtX+9W9MuRG7iMj1UqATWQAGggO80v5KdO+3+rZ6vIPOOo7spGzK88t5uORhygvK2Zy7mWRPcpxHLFdlLfRdGQlsHedGvu66CDY0cm5aAeSVwoYHIbfU+Tq3FL7zbmft3FhZxQpzN0nY78d/4gT+2jr662rx1zdEN8F25+SQUlFB9nseJ6WiguQtW+bk/mdyc8xkI3YRkako0InMQ239baOqb6c7ThO0ziL7NVlreNuKt0Wrb6szVy/Y/aYWhCGfE9aiVbbXI8HtHAzFTJf0JENuCRRugy2POV/nlkLuOkjJnvja93xx/Bq6hBSnMYrcEIErrfjraqPbBwycPh1teJG4bh0Z990b3cA7cbV+Fheztc88He8hiEiMZ+qa+Mufv8blbj/Ls1P47Ntv4eGK+bnXowKdSJwdPH+Q/bX7afG1UJhWyN7KvexZuyd6PBQOcbb7bLTyVtdaR1NfEwBJ7iQ2527mQ5s/RHlBOWX5ZeQk58TrpchkwiGn5f9wYItOlTwLPU0xJxrIWuGEtPL3RaptkeCWWQTXupZquJvldLpcStRE+4QB2FCIwTNnnMYlke0DAk3O988kJZGydSu5H/kIKRXlpJSX48nRz6KIyFz0TF0TnztwAn/Ame3S1O3ncwdOAMzLUKemKCJxdPD8QfYd2cdAaCB6X7I7mfdvfD9JniTqW+s53nacvkAfALnJudHKW0VBBRuXbCTBre5oc0Z/Z0y1LWaqZMc5CI00QiApaySo5ZbEfL3OqaBJXDXv+yO6f/ADsh57jMx3PBANb/6GBsI+HwDu/Lxo5S21spLkDRswiYlxHrmIiIwVClt6BwJ4/QG6+wN0+wP89vfr6OoPjDu3KDuFw7//tjiMcjx1uRSZJ+7/0f00+5onPGYwlOSUUJE/0n2yOL1YU7bibbghSTSwnR2pvPV3jJzn8jjdImOrbLklzvq2tHzt0TYHhbq76fnFL2j54r6R1vIAxpC0fn20cUlKZSUJRUX6WRQRmUVDwTBefwCvf8gJZpFw1t0/RI9/+Gvns7d/KHq7ZyDAdOOOAd74yp6rnjcb1OVSZJ6YLMwB1LyvhszEzFkcjURZC70tTliLXdvWcXaKhiR7RjckyVkFqp7OaaG+PvqPHqX/ly/h++WLDJ5+lVH/13e5SNu1i6K//AvcGRnxG6iIyAJhrcUfCNHdP1Ixiwa0sbcj53gjoc03FJr0usZAVkoC2SkJZKUmkp2ayOq8tJHbKQnO8VTn46nv1tLaOzjuOsuz5+csGQU6kXlFrqwAACAASURBVDg47z3PV49+ddLjy9KWKczNhiFfzHq2cze2IYnMOWG/H39dHb4Xf0n/L3+J/5VXIBTCJCSQUl7Okl/7MF3f+W50TzjCYfpfeAE7MAAKdCIiUeGwpXcwiLc/QHdMIPP2D42a2jg2sHn7AwyFwpNeN8FtyE5NjIaz5dnJbFyW6QSxSCAbDmjZqZGQlpJIRrIHl2v6syb+4MGNo9bQAaQkuPns22+Z0fsSLwp0IrOoc6CTr9d/nR+d+REpnhQeWP0A/3Xpv8atodtbuTeOo5zjjv/w2pp8jG1IEru27WY2JJG4s0ND+I8fdwLciy/ib2hwwprb7TQw+dhHSbv9dlIqKnAlJ9O8748YOytH+4SJyHxxPV0bA6FwTBVs/HTG2KmL3f6AM7UxEtrCU0xjTE10j6qOlRSkk5WSQFaqE8CGA9qo26n/P3v3HR9Vlf9//HWmZWbSQyCUAAmC1IQOAQQVFVQUUVnXdVXUteG68rWtoKuyVuxlBV1XcXVtqwiKYsG1/BREILTQRSCQhEACIQlJJpl2fn/MJJlJASRlkvB5Ph55ZO6dO/eeyUwg7znnfI4Zm9nYLMPZK38ubaXKpcyhE6IZVHgqeHvL27y28TUcbgdTT53KLYNuIc4ad8wqlyJAxgd1l+G/8EXoeXZw9cjKoZIFu6QgyUlCu92Ub95c1QNXtnatr3dNKaz9+mEfOZLwtJHYhgzFGBFe6/G7plxMxbZttfaH9ekjJeeFEC2W1poPVmfxwOLNVLire7/MRsV5/TuSGGev6h0rrDGcsaTCfdRzR1lNxNgt1b1hNXrHqvYFBLRom5kwk7Gpn3abJ0VRhGghtNZ8mfklz695nn2l+zg98XTuGHoHPWJ6hLpprY+zFP4xFI7UMe9QGUAHDOGQgiQnBe31UrF9e1UPXFl6elUVyrBevbCnpRE+cgT24cMxRkeHuLVCCFE/rX1DGIsd1fPGAm9Xf7mD7q88xn2U7jKTQQX0jlWHspq9Y5XhrHK4Y5TNjPE3DGMUjUuKogjRAqzPW89Tq58i42AGvWN7868x/yKtU1qom9WyuJ1Qmg8lB6Akr/p7aV7tfc6S+s+jvXDOw1KQpI3TWuPcuZPSlSsp+3klZatW4SkqAsDSvTtRF1xAeNpI7CNGYGrXLsStFUKcbLxezZFyd60QVlxeO5jVDGvFxxjCaFAQZavuFYu2mUmMtVXdfvn7nXU+TgE7Hj1PqvK2cRLohGhkWcVZPLf2Ob7e8zXtbe15aPRDTD5lMkbDSTL8wOsFR4E/iNUIZVX7/CHOUVD3OazREJHg++o82FdJMqID/PSPuh8T3RXG3Na0z0s0O601rqwsSn/+2V+JciWegwcBMHXuRMRZZ/l64EaOxNyxY4hbK4RoCzxeXU/PWECvWB0BrajMxZEK91HL41f1lPl7v2LtFrq3CyfaZgoKapX3B26HW45e9GPx+n3kFDpq7e8cY5MwdxKQQCdEIymqKOJfGf/inW3vYDaYuWXgLUzrPw272R7qpjWc1lBRXCOc1RXU8nw9brqO0sImmy+URST45qt1H+0PbR0CvnfwhTezte52RCfWPYfuLClY0Va4cnOreuBKV67EnesbYmtsH094WpqvB27kSMyJsiajEG3diRT5gOpCH3X1gtUd1NxVwxePHGNOmcVo8IctXwhrHxFGz/YRtYJYzUAWbTNjtzRdwY+7J/ZuU1UbxW8jc+iEaCCX18UH2z/g5Q0vU1xRzEU9L+Ivg/9CB3uHUDft2FyOGuEscMhjjX3u8tqPN5iqe88iEiCifXXPWtU+/21LROPMXfutVS5Fi+Y+eNAX4Pxrwbn27AXAGBODfcQI7GkjCU9Lw5KcLAFOiJPIorXZzFq0kXJXcJGPCwd2Jqld+FGHMJYdZb0yAKvZENwbZq0/hEXbg4+zmg0t9t+iEw3AomVqtqIoSqlzgRcAI/Ca1npOPcdNBT4Ehmutj5rWJNCJ1kJrzbdZ3/LcmufYU7yHkZ1Gctewu+gT1ye0DfO4oexg3b1nQb1reVBRVPc57O3q6D3zh7PwgNBmi5Vy/uI38RQWUrp6tX8O3EoqdvwKgCEiAvvw4VU9cGGnnoqS95YQbYrWmiKHi4MlFeQfcXKwpKL664iTQ6UV5Jc4OXikos7hg4HsFmO9wxN94csUFMYCj5MKjKI1aJaiKEopIzAXOAfIBlYrpRZrrbfUOC4SuA1YeaLXEqKl2XxoM0+vfpr0A+kkRycz96y5jO0y9sQ+tTueHietwXG49hy0uuaolR2CWqtpAWFR1cEsoT+cMr52L1pEAoTHS0ER0Wg8JSWUpadX9cBVbN0GWqNsNuxDhxI1eTLhaWlY+/ZFmWQWgBCtjcerKSitDmeHSny38/0hLTC0HSpx1lmN0WhQxIVbiI8IIz7Cwinx4Sxcl1PH1XxFPrY/ch4Wk3zgI0SlhvzvOQL4VWu9C0Ap9T5wEbClxnEPA08CdzXgWkK0CPtL9/Pi2hf5dNenxIbFct/I+7j01EsxG04wANVcV60oCz6eDmvehLCI4B41r6v2441h1WEsNgm6jgiej1Z5O7wDWNrAXD7R4nkdDhzr1lWtBefYtAk8HpTZjG3wYOL/cqtvMe+UFJTFEurmCiHq4PJ4awSzCg76tw+VVN8+WFJBQamzzuqMFqOB+AgL7SLC6BAZRr9OUcRHhlWFtvYRYbTz3461W2oV/Fi5u6DeIh8S5oQI1pBA1wXICtjOBkYGHqCUGgx01Vp/ppSqN9AppW4EbgTo1q1bA5okRNModZXy+sbXeWvLW2ituW7AdVyfcj2RlsgTP+mBLbDkjuACHwBeN+z9CRIG+AJZh351D32M6ODrdWuhY/nFyUE7nTgyMqrWgnNs2IB2ucBkwjZgAO1uuJ7wtDRsgwZhsNZT7EYI0eTKXR7yjwT3oh30h7Pq0FbBoVLfwtN1sZmNxEf6etK6xtkZ3C2W9v7QVhnUKkNblNXUoLlmUuRDiOPXkEBX129p1Wc0SikD8BxwzbFOpLV+FXgVfHPoGtAmIRqVx+th0a+LeGndSxwqP8R5yecxY8gMukSc4CTjwizYtAAyPoS8zfUfpzXc/OOJXUOIJqTdbso3b6Z05SrfYt5r16LLy0EprP36EXv1Vb4euCFDMUaEh7q5QrQojVm0QmtNqdNTFcR8vWm++WeHSmsOd3RSUk/1xkiryd9bZuHUhEh/MAurCm5VQS0ijPCw5hsWXflzkSIfQhxbQ34zs4GuAduJwL6A7UhgAPC9/xOajsBipdTkYxVGEaIlWJ6znKfTn+bXwl8Z1H4QL45/kdT2qb/9RGUFsHkRbFzg63kDSBwB5z0Fy5+D4n21HxOd2LDGC9FItNdLxfbtVT1wZenpeEtLAQjr1YuY3/3OV8hk2DCM0dEhbq0QLdfH63KCepxyCh3MWrgRqA4vgUVDqoY1Hgke4phf4vQPe6wIqgAZKNZurgpjKYkxVYGs+nsY8ZFhtAu3YDW33AIhUwZ3kQAnxHFoSKBbDfRSSiUDOcDlwBWVd2qti4D4ym2l1PfAXRLmREu34/AOnkl/huX7lpMYkcgzpz/DOd3P+W1DR5ylsP0L2Pgh/Po/3zDK+N4w/m8wYCrEJfuOs8XIumqiRdFa49y5s2otuLJVq/AU+aqhWpKSiLrgAl+AGzECU7t2IW6tEC2L1ppyl5cyp5sypweHy0OZ00OZ083Dn20JGj4I4HB5uOejDF5btquqyqPLU3/RkHbhFtpHhtEjPrxWOKvcjgu3YDbKHDMhTiYnHOi01m6l1K3AV/iWLZivtd6slHoISNdaL26sRgrRHA46DjJ3/VwW7lhIuDmcu4bdxR/6/AGL8TgLN3hcsOt7X4jb+hm4SiGyM6RNh5TLoGNK7fluldUsZV01ESJaa1xZWZT+/LO/EuVKPAcPAmDu3JmIs86qXsw7ISHErRWi4dweL2UuDw5nddgqrwpewfsdTk/Ase7g+10eyp0eylzuqn0Ol4ffuhpUhdtL+4gw+naMquo1a19VPKT+oiFCCFFJFhYXJ71ydzlvbXmL1ze+jtPj5Pd9fs/NqTcTY4059oO1huzVvmqVmxf51n+zRkO/KZDyO+g+RtZpEyHnyssj5447SXzuWUzt2+PKza3qgStdtRL3vlwATO3bY09LI3zkCOxpaVgSZeivOLqmWMhYa02F2xsQptx1h62gEFa97Qg81uWutc/pqXuYYn3MRoXNbMRuMWG3GLFZjNgtRqxm33e7xeTb59+21TjObjFiM5u47b115JdU1Dp/lxgby2eOb9DPTAjR9jTLOnRCtHZe7WXJriW8sPYFDpQdYHzX8dw+9HaSopOO/eC8bb6euI0fQuEeMFnh1HN9PWs9zwZTWJO3X4jjobUm7+lncKxZw55rrkW7Xbj27AXAGBODfeRIwm+4AfvIkViSkxtUlU6cXHxzwjJw+Odx5RQ6+OuCDDbmFJGaGB3Ua1Wrd8vpweGqva/cf2xdZfCPxmo2+IKVOSBEWYx0iLTWGbYq77fVDGE1Q5rF2GjDF++b1FeqNgohmoT00ImTUvr+dJ5Kf4oth7bQr10/7hp2F8M7Dj/6g4qyYdNHvgqVBzaCMkCPM3w9cX0uAGtUczRdiDp5SkpxZmbi3L3b95W5m4rMTCp2Z4Kjeo6mffRoIk8fhz0tjbBevVDSgyyOoqTCzb5Ch/+rvOp2TqGD9D2H8Rxn8jIaFHZzda9VYLCymoN7smr1bllMAYEsuKfMZvZ9tZbhiE3RoymEaJukh06Ieuwp3sOz6c/ybda3JNgTeOy0x5jUYxIGVc8ftWUFsOUTX4XKPcsBDV2GwrlPQP+LIVLmFInmo91uXDk5VOze7Q9v1QHOnZ9ffaBSmLt0wZKcjHZ7cO7cCR4PmM1Yuncnbtq00D0J0WK4PV4OHKmoN7DtK3RQXB5c6t5oUHSMstI5xlpvmFPA13ecHtQTZjEapPcXqdoohGgaEujESaGwvJBXMl7hv9v+i8Vo4S+D/8JV/a7CZrLVPtjl8FeoXAA7loLXBe16whmzIGUqtDul+Z+AOKm4Dx+u7mnb7etpc+7OxLl3L7iqF/w1RkdjSU4m/LTTsCQlYUlOIiw5GXO3bhjCwnDl5bHznAm+MAfgclG0cCHtb5mOqX370Dw50Sy01hQ73FXBbF9RZUgrJ9e/b39xea2hjTF2M52ibSTG2hiRHEfnGBudY2x0ibHSOcZG+4gwTP4hiGPmfEtOoaPWtTvH2OjZIaI5nqYQQggk0Ik2zulx8t629/hnxj8pdZVySa9L+POgPxNviw8+0OOG3d/7QtzWT8FZAhEdYeRNvhDXaVDtCpVCNIC3ogLnnj21etqcmZlVywQAvl61bt2wJCcROf5MLEnJWJKTsCQnY4qNPeo1Ds57Ge0NLgChvV7y571MpwdlaYzWrMLtYX9ReVCvmi+0VW+XOYNL5FuMBjrFWOkcbWPUKfF09oe0ysDWKdr2mxaOvntib5kTJoQQLYAEOtEmaa35es/XPLfmObJLshnTeQx3DruTXrG9Ag+CnDX+CpULoTQfwqJ9QylTfgdJp4Gh5S64Klo+rTXuAwd8vWw1hkm6cnIIrG9u6tABS3IykeeeW9XTZklOxty5M8p0Yv9UO9avD+rRA8DlwrFuXQOelWhqWmsOlToDhj/6e9UCAlv+kdrVEuMjLL7esfYRjOvVPiiwdY6xEh8e1qhzzSqHDsqcMCGECC0piiLanIz8DJ5Of5p1eevoGdOTu4bdxZguY6oPyP+lukLl4d1gDINTJ/orVJ4DZmvoGi9aJU9JaVXvWlVBkt2ZODMz0QEFSZTdjiWpO2FJvrBmSU72DZVMSsIYEd4kbZMiDC2Pw+lhX5EjKLBV3s4tKien0IHTHdyzajUb6FIZzqKrQ1rlvo7RVqxm+QBKCCHaCimKIk5K+0r28fza5/li9xe0s7bjwVEPMqXnFEwGExTv81Wo3Pgh5G4AFCSPg3F3Qd8LfWvHCXEUQQVJKodI+gNcUEESg8FfkCSJ8BHD/XPbfOHN1KFDsxaG8JWVrx4Sl1PoYNbCDAAJdfVoaAD2eDX5RyrIKXSQW1RdbKRqLluhg8Nlwb2mSkFCpK/QSP/OUZzTL4HO0YHDIW3E2M1SVEQIIUSdpIdOtHpHnEd4beNrvL3lbZRSTOs/jesGXEe42wVbF/uGVGYuAzR0HuwbTjngUojsGOqmixZGa42nsiCJP6xVVIa3rKw6C5JUfyURlpSEuXt3DBZLs7bb7fGSW1RO1uEysgscZB0uI6ugjM837q93EeXK9bXMRgMWo8JsMgRv+2+bTTW2jQYsphrb/vtNRgNmo8IScC6zUWEJOFfQdp3nMmD27zMZVEgDMPjmhD1+SUpVqDtS7qqat5ZTo1dtX6GD/UXluGtUGokMM1X1qAWGtE7+0NYx2tpoa50JIYRoG35LD50EOtFqub1uPvrlI+ZtmEdBeQEX9riQ21JupGPOel9P3I6l4HFCXA9IucwX5OJ7hrrZogWoKkhSo6etIjMTb0BBEmU2Y+7ezTefLSnJX5DEF96OVZCkUdvr1eSXVJBVUFYjtPm+5xaVB5WQNyjoFG2rswJhpRvGJuPyaJweLy63F5fHW71d+eWuse3RON3V227/8U6Pl6b6r8TiD4WVgbNq+yiBM/D++sJn9fmq739kyZZavWcAYSYDyfHh5BQ6OFKjjL/JoOgYbfUPgwwObJ1jbHSKsRJlNTfND0cIIUSbJUMuRZumtebHnB95Jv0ZdhXtYljCUOb1uY7+u1fCy2OhohgiEmD49b4KlZ2HSIXKNsaVl0fOHXeS+Nyz9Zbf11rj3r8fZ2Zm8DDJ3btx7dtXZ0GSqPPOrQ5vycmYu3RBGZt+XpLWmsIyV1BI84U3B9mHy8g+XHtOVfvIMLrG2hjaPZausXYSY210jbPTNdZOpxhfj099ZeW7xNi4b1K/Rn0OHq/G5Q93voAYsF1nOPTidOvgbY8OCJf+7YDAWbVdx+PdHk2Z01Vv+KwMqzV/jsejwu2la5ydtB7tqnrVKkNb+8gwjK1kUWshhBBtkwQ60apsL9jOU+lPsTJ3Jd3tCbwQPYwzNy1H/bwILJHQb7KvJy55nFSobMMOznsZx5o15M97mQ533uELa5m7q77XV5AkLCkJ28CBRE+ZEjRM0hDeNAVJApVUuH0hrcAX0CrDW2VgK6kI7vmJsZtJjLXROyGSs/sm0DXWRmKc3fc91n5cBTCas6y80aAwGowtvjCH1tofPmv3Rk595Sfy6qge2SXGxr+uPq4PSYUQQohmJ4FOtAp5ZXm8tO4lPv71Y6IMZmY6DFy2ezVm4wboNcEX4k6dCOY6FgoXbYanqIjipUsp/PBD0JrC996j8L33qg+oWZAkoLetqQuSlLs85BQ6qnvWKodHHvbtqzmUz24x0jXWTtc4G2k92gX1sCXG2RplmJ6Ula9NKYXJqDAZwUZw+Lz3/L6yrpoQQohWR+bQiRatzFXGm+vm8sa2d3F53fyxuJgbCouJ7jbGt8xA3wvB1nxzmZqClJWvn7e8HMfatZSu+JnSFSso37w5aKgkBgPWlBTaXf8nwpKTMXfr1mQFSaoKjwT1sPnCW1ZBWa2eHYvRQJdYW1BQ6xpnqxoeGRdukaqFLZD8PgohhGgJpCiKaPU8ZQUsXjGHl7KXkoeHc0rLuN3Uka4DLvdVqIzqHOomNorjqap3MtEeD+VbtlD60wpKf16BY81atNMJJhO2gQOxpaZy+O230QHVJlVYGD3/93W9c+mOV83CI1kFjqDwVl/hkeqQ5g9s/vDWIbJxF3EWQgghxMlDiqKI1slVDjuWsnL9fJ4u3cY2i5lUNzzd+SwGD5sO7dvesKenvtoWFOYAHC4PD3+2hYQoKxaTgTCTAYvJV93PYgr4Mvrua829PFprnJmZlK5YQdmKFZSuXIW3uBiAsN69if3DHwgfPQrb0GEYI8LJnf13PFoTWODd4/GQP+9lOj34wDGvdbjMVWcPW3ZBGdl1LOZcs/BI1zjf/LXAwiNCCCGEEKEkgU6EltfjWyNu4wfs+mUJz0aY+H92G52tUTzZdxrnDpmOMrSdP5o9Xs3W3GLSMwtI33OYnMLyOo87VOrkD//6+bjOWbmuV82wZzEZfYHQWHN/ZTn3esJiwO2wY95nrHVds/Hoa4e58vIo+/nnqmGU7v37ATB17kTkhHMITxtFeNpITPHxtR67f8VqrO7g4iEGt5v9P62iE8GFRyqHQmb7K0VmFZRR6gwOzzF2M11j7fTpFMnZ/QILj/iGRbb0Ah9CCCGEEDLkUjQ/rSF3PWxcAJs+oqD0APPaxbMgworNaOX61Bu5sv/VhBnDQt3SBitzulm/t5DVmYdJ31PAur2FVdUMO0VbKSxz1eqhA4iPsPDi5YOp8JdZr/ryBN+uCLrP4y/T7ivXXlF1vKf2Y2s+vpHXEQsMflGeCgYc3En//dvpnbOdTof3+X421nCyuvcjp8cA9vdMobx9Jyzm6oBYO4gaefKrbRTWsU6Y2agIDzPVui+w8IhvSKS/vL9/X6SsDyaEEEKIFkiGXIrQyvgAvnkIirIhOhHOesBXwOTQTl+I2/ghHNpBhcHMOz0G8S8dgUO7mXrqVG4ZdAtx1rhQP4MTlldcTvqew6T7A9zmfcV4vBqloHdCJFMGd2Z4UhzDkuLoEmOrdw7d3yb1Y3TP2j1UTUVrjdurawXHijqDZGVADDje7ak6xu2owL5rG3HbN9DhlwzaZf+KwevFZbKQ3fVUvk05jV8S+7I3rgsVHnyPK/XiLDpUK7AeL5dHc35Kp6DCI13j7MTaza16SKoQQgghxLFID51oXBkfwKe3gStgMWODGaK6QGEmALr7GL7slsLzBWvZV7af0xNP546hd9Ajpkdo2nyCtNbszC9hdeZhVmcWsGbPYfYcKgMgzGRgUNcYhifFMTQpliHdYom21d0b1Nqr6mmvl4rt232FTFasoGzNGt/6bwYD1pQBhI8aRfio0dgGD/pNFSi1rlwnrDo4XjR3GQeK614nbPnM8Y35tIQQQgghQqbZqlwqpc4FXgCMwGta6zk17r8Z+DPgAUqAG7XWW452Tgl0rdxzA6Aoq/Z+gxnOup/1nfry1NY3yTiYQe/Y3tw1/C7SOqU1fztPQIXbw8bsIn8PnG8OXOUQv3bhFoZ2j/X3vsXSv3M0FlPbmftXkzMri9IV/gD380o8hw8DYDnlFMLT0ggfPQr78OEYo6Ia9bpSFVQIIYQQJ4NmGXKplDICc4FzgGxgtVJqcY3A9q7W+hX/8ZOBZ4FzT/SaohUoyq5zd5ZB87xrL0t/fI32tvY8NPohJp8yGaOh5RadKCxzsmbPYVZnHmbNngI2ZBdVVUHsER/OhH4JDOvuC3DJ8eFtemifu6AgqJCJK9v3Ops6dCBi3DhfgEtLw5yQ0KTtkIWyhRBCCCGCNWQO3QjgV631LgCl1PvARUBVoNNaFwccHw60rPGdonHlbQODkSU2Cy/ExrDfZKSDx8OpTic/22yYcn7kloG3MK3/NOxme6hbG0RrTVaBg/Q9Bb4CJpkF7MgrAcBkUKQkRjNtVHeGJcUxtHss8RGtv2DL0XjLyihLT68KcBXbtgFgiIzEPmIEcddcQ/joUViSk5s9yE4Z3EUCnBBCCCGEX0MCXRcgcGxdNjCy5kFKqT8DdwAWoM5JLkqpG4EbAbp169aAJomQ2fwxfHwLSyKjmB1jp9y/1MABk4kDRiNDI7rx5Pn/poO9Q4gb6uP2eNmae6Rq7tvqzALyjvjmZkVaTQztHsuUwV0Y2j2WgYkx2CwttyexMWiXC8fGTZSu+ImyFT9TtmEDuFwosxnbkCG0/78ZhI8ahbV/f5RJaikJIYQQQrQUDfnLrK6P5Wv1wGmt5wJzlVJXAH8DptVxzKvAq+CbQ9eANonm5vX4Kloufx4Sh/NClKbckRd8jFLswx3SMFdSUbl8QEHV8gFl/jXJusTYGHVKO4YlxTE8KZZTO0RiMLTd4ZPgLzjy66++eXA/raBs9Wq8paWgFNa+fWk37Wrso0ZhHzIEg80W6uYKIYQQQoh6NCTQZQNdA7YTgX1HOf594OUGXE+0NGUFsOA62PUdDL0W98RHyX2v7gIn+0v3N2vTDhSX+8Kbf/mALfuK8WowKOjTMYrfDU1kaFIcw7rH0jnm5AgsrtxcXyXKn3+m9OcVePIPAmDu3o2oCy8gPG0U9pEjMMXGhrilQgghhBDieDUk0K0GeimlkoEc4HLgisADlFK9tNY7/JuTgB2ItiF3A/z3SjiyHyb/g5xTz2LWN9PrPbxjeMcma4rXq/k1vyQowGUV+JZNsJmNDOoaw61n9mRYUhyDu8WcNItJe4qKKF250leJcsXPODMzATC2a1dViTI8LQ1zF5mPJoQQQgjRWp1woNNau5VStwJf4Vu2YL7WerNS6iEgXWu9GLhVKXU24AIOU8dwS9EKbfivb605ezu49ks+dx7g4cVT0Wh+d+rv+HTnp5R7yqsOtxqtzBgyo9EuX+7ykJFdRPoeX4Bbs+cwRQ7f8gHxEWEMT4pl2qgkhifF0a9zFGZj210+IJC3vBzH2rVVhUzKN28GrVF2O+HDhxNz+e8JHzWasFN7temKnEIIIYQQJxNZWFwcP48LvroPVv0Tup9GyZS5PLbpVT7d9SkD2w/k8bGP0zWyK0t2LeGFtS+wv3Q/HcM7MmPIDCb1mHTCly0o9S0fULn228bsIpwe3/IBp7QP96/95hs+2b2d/aQJK9rjoXzLlqoFvR1r16KdTjCZsA0c6F/QOw1bSgrqNyzoLYQQQgghQqvZFhZvChLoWqgjB+DDa2DvT5B2CxsGTWXm8r+xr3QfN6beyE2pN2EyNLz6odaaPYfKqhbvXp1ZwM78UgDMRkVqYgzDusdWLR8QF37yBBWtNc7dmZT+vIKyFSsop4Nk8QAAIABJREFUXbkKb7FvZZCw3r2rhlHahg7DGBEe4tYKIYQQQogT1SwLi4uTSNZq+OAqcBTiufhV/sVhXln6JxLsCbwx8Q2GJAw54VO7PF627CsOWD7gMAdLfMsHRNvMDO0ey6VDExmeFEdKl2is5ra5fIArL4+cO+4k8blnMbVvH7Q/cEFv935fcRlT505ETjiH8LRRhKeNxBQfH6qmCyGEEEKIEJJAJ44u/Q34/G6I6sy+P77LrG1vsjZvLecln8ff0v5GlCWq1kM+XpfDU19tZ1+hg84xNu6e2LtqIegj5S7W7S30974dZn1WIQ6Xb/mArnE2xvWKZ2hSLMOT4ujZPqLNLx9Q6eC8l3GsWUPeCy8QOX68P8D9hPPXnQAYo6Oxp6URfvPNhI9Kw9yt20kztFQIIYQQQtRPhlyKurnK4Yu7Ye1bcMpZfDH8Dzy89jm8eLlv5H1c0OOCOgPFx+tymLVwY1VIA99QybTkOA6Vuti2v3r5gH6doxjWPc4/By6WhChrcz7DJqU9HrwOB96yMrTD4b/twOsI2HY40A4HroMHKZj/Bniqf2bKasU+dCjho9KwjxqFtW9flOHkKO4ihBBCCHGykyGXomGKsuGDqyFnDaVjZvCY1cXilQ+R2j6VOWPn0DWya70Pfeqr7ThcHmLLi5m5+m3mDL+Sw9Yolv16iDE94/nL+F4MT4pjULcYIsJC9/bTWqOdTl/gKi+vClzaURYQvgICWOB2WXUg891fXh3QynyP107niTXMYCDijDPo8vxzGKSQiRBCCCGEOAYJdCJY5jL4YBq4K8iY9Dj37P2Ufbn7uHngzccsfOL1anIKfeu/XbHtawYc2s0ftv2PeYMuAeDt60f+pqZorzeoNysocNXs8Spz4C131Ahf/uNrBrAyB97y8qAeseOhwsIw2Gwouw2DzY7BZvN9xbfDHLhtt6FsNgzWgNs2Owa77/7AbW9pKbsvuRRdUVH5Q6R0+XK8RUUYAubSCSGEEEIIURcJdMJHa/j5ZVj6Nzxxybw27FJe3voqHewdjqvwya78Eu75KAOAWEcRE/euwoDm3D0rybPHEGkPI39uZq3AVbPHK2g4Ynn5Ua9Zi8FQO3BZrSi7DXNcXHDYqry/jsDlC13+bavVd9tmRRkbvyBL7uy/o73eoH3a6yV/3st0evCBRr+eEEIIIYRoWyTQCXCW+RYK3/ghub0nMDM6jLW/vMd5Sefxt1F1Fz6p5PFqXl+2i2eW/kKYycCfu7gY/drzmL2+3i+z9vCnLZ8DcDAdlNmMsgf0ZvkDmDE2FnPnzrUDmd2Gslrr7eGq2rbbURZLqysU4li/Hlyu4J0uF45160LTICGEEEII0apIoDvZFeyG/14FBzbx5Yg/8lDheryFXh477bF6C59U+uXAEe5ekMGGrEImd7fxf3u+oWLuQmqW2fGaLfRc/DGWrl1RJnnLBerx8aJQN0EIIYQQQrRi8tf1yezX/8GCP1GqNI+NuITF+T8eV+ETl8fLK9/v5MVvdxBjMfBW1C4S5v+HCoeDsN6nUrFrd1CvkwFNwVv/kSGEQgghhBBCNDIJdCcjrWHZs/DNw2R07M3MdlHkHFzDTak3cdPAmzAbzPU+dFNOEX9dkMGW3GKmh+dz8U8f4tm9C9uYMSTcO4ucO++SIYRCCCGEEEI0Ewl0J5uKI7DoZjzbPuP1U0czz51LB6WYP3E+QxOG1v8wt4d/fPMrL/+/nfTxFvFRzjfYVy/H0K0bnebNJeLMM1FKyRBCIYQQQgghmpEEupPJwR3w/hXkFmYyq99I1jiyj6vwybq9h/nrggyy9h3ikaJVDPr5c5TZTPwddxB3zTRZL00IIYQQQogQkUB3stj6GSy6mS/D7TyUnIzHVcSjpz3KhT0urLfwicPp4dmvt/P6j7u46OBGntvyOcaCg0RNvpAOd96FOaFDMz8JIYQQQgghRCAJdG2d1wPfPUbpsmd4vGtPPjFWkBrTy1f4JKr+wicrdx3ino8yMP+6nTd2f0GHvb9gHTCAhLkvYh88uBmfgBBCCCGEEKI+EujaMsdh+Oh6Nu79gXt69CJHO7kx5UZuHnhzvYVPSivcPPHlNj75fhN/3vk1p+1YgSkujg6PPkL0xRejDIZmfhJCCCGEEEKI+kiga6v2b8Lz3yt4XRczr0snOthimT/28aMWPlm24yD3frCWYev+x1u/foPF7STummuIv2U6xsjIZmy8EEIIIYQQ4nhIoGuLNi5g/5IZzGwXwxpLJOcmTeT+UffXW/ikuNzFY0u2suOzr/n7lk/pWHSA8LFjSZg1i7Aeyc3ceCGEEEIIIcTxkkDXlnjc8L8H+WrD6/y9Ywc8ZiuPjLyPyadMrrfwyTdbD/DCm99xyc8fMm3/FkzdutFxzjwizjij3scIIYQQQgghWgYJdG1F6UHKPryax0u28nFCe1Lb9WfOuCfrLXxyuNTJ4x+tIfzD//Dozh8xhlnocNedxF59tSxDIIQQQgghRCshga4tyFnDxo+uZqbdS1ZkBDek3MD0QdPrLXzyxYYcvnzh3/xu7WLalRcTedFFJNx5B+YOsgyBEEIIIYQQrUmDAp1S6lzgBcAIvKa1nlPj/juA6wE3kA9cp7Xe05BrimCeNW8yf9mDzIuOJN7WnvmnP82wjsPqPDb/SAVzX/mE1IXzufnwHnTvfiQ99AC2gQObudVCCCGEEEKIxnDCgU4pZQTmAucA2cBqpdRirfWWgMPWAcO01mVKqenAk8DvG9Jg4ed2sn/JDGbl/o/0mEgmJp7J/ac9THRYdK1DtdZ89v0msp96ht/vWklFVAwJjzxC7CWyDIEQQgghhBCtWUN66EYAv2qtdwEopd4HLgKqAp3W+ruA438GrmzA9USl4ly++vAy/q4K8NgjeCTtfib3urjOIia5B4/wyQPPMvyHRSR53Rj+cCUpd87AGBERgoYLIYQQQgghGlNDAl0XICtgOxsYeZTj/wR8UdcdSqkbgRsBunXr1oAmtX1lO7/j8a//zMc2IynhXZkz4VW6RdX+mWmt+fK1hVhefYHTj+RTkDKcfk/8HZssQyCEEEIIIUSb0ZBAV1dNe13ngUpdCQwDTq/rfq31q8CrAMOGDavzHCc9rdn0w6Pcs+MdsqwmbuhxMdPH3F9n4ZO9GdtYP3M2vXZt4GBsApannmfMhRND0GghhBBCCCFEU2pIoMsGAmviJwL7ah6klDobuA84XWtd0YDrnbQ8FSW8sehy5pZnEm+2Mv/M5xjW7Yxax7mKj7DswSdp99UiuhhMZP3+es6adStGa1jzN1oIIYQQQgjR5BoS6FYDvZRSyUAOcDlwReABSqnBwD+Bc7XWeQ241klrf+4aZn15PekGNxPDu3P/Be8QbYsJOkZ7vex8+wMKnn+OjmXFbBgwltFz/sbQnjJ8VQghhBBCiLbshAOd1tqtlLoV+ArfsgXztdablVIPAela68XAU0AE8KG/YMderfXkRmj3SWHpyuf4+5bXcQEP9/g9F532t1qFT0rWrWfLvQ8SufsXctt158A9j/L7y86qs0CKEEIIIYQQom1p0Dp0WuvPgc9r7Hsg4PbZDTn/yarMWcqcz65m0ZFfGKCNPHH2XLp1Oy3oGFdeHjsfexL95RJcYZF8ccGNXPG3m+gYYw9Rq4UQQgghhBDNrUGBTjS+zbmruefr6ez1lnODuSPTL1uAOWCIpdfp5OC/3+TA3JfRLidf9DuLvnfP4Pa0ntIrJ4QQQgghxElGAl0L4fF6eGP1M8zd+h/aedy83m0yw8+eAwEh7cj335P18KOonGzSO/Zjx6V/4o5rzyI+QoqeCCGEEEIIcTKSQNcC7C/dz71Lb2Z18U4mlLt44Mynie59QdX9Fbt2k/v44zh+/JGciPa8O346l03/HdemdAphq4UQQgghhBChJoEuxL7evZTZy2bhcpfzkDucKZd9iopLAsBz5AgH573Mobfeotxo5j8DLsR48WU8OyWV2HBLaBsuhBBCCCGECDkJdCFS5irjiRUPs3D3ZwyoqGBOXBrdL/onmG1or5eiRYs48MyzeA4fZmm34Xw+Ygoz/zia8X0SQt10IYQQQgghRAshgS4ENh/czD3f3c7e0lyuLz7CLSP+innkdFCKsnXrOPDoY5Rv2sSuDj14ftxVDJs4mgXn9yXKag5104UQQgghhBAtiAS6ZuTVXt7Y9AYvrXuRdi4Xrx/xMPzid6H7aFwH8sh/9hmKPllMWVQcLw27gh0DRjPn0oGc1is+1E0XQgghhBBCtEAS6JrJ/tL93Pfjvaw6sJpzSst40NKd6OvexmuNp+DVf3HwlVfwOl18kTqBf3U7ncvH9ealib0JD5OXSAghhBBCCFE3SQvN4H97/seDyx/A5SrhofxDTDn1d3DeExz58ScOzHkC19697O07jNmJZ2Pt3p23Lk1lRHJcqJsthBBCCCGEaOEk0DWhMlcZT65+ko92fER/NzyRl0/3iU9SET2GAzffSuny5bgTu/PC2X/m28hkbhjbg9vPORWr2RjqpgshhBBCCCFaAQl0TWTzoc3M/GEme4ozub64lFtcVgyXfMiBhcspeGcKympjxblX8ahlAKd0imbh1IEM6hoT6mYLIYQQQgghWhEJdI3Mq738e/O/+ce6fxCHkddzDzCsw3AKLVPJv3YmnsOHOXLW+dwTM4Ysbxh/PuMU/jy+J2Em6ZUTQgghhBBC/DYS6BrRgdID3LfsPlbuX8k52saDe3dgjr+MzM/yKd/yJKaBg/jvpXfw73wr/TtE8cnUVPp3jg51s0Ub5HK5yM7Opry8PNRNEUIIcZKxWq0kJiZiNstyS0I0Bwl0jeSbPd/w4IoHcbrLeeiIm0n7ssjPm0Dxuz9gSkgg99ZZ3HkogdICL3dP7MWN43pgNhpC3WzRRmVnZxMZGUlSUhJKqVA3RwghxElCa82hQ4fIzs4mOTk51M0R4qQgga6BggqfWBOYs3sHETvi2ZWRAN5tWK+9nifiRvLlziIGdY3gqamp9EqIDHWzRRtXXl4uYU4IIUSzU0rRrl078vPzQ90UIU4aEugaoLrwyR7+ZElk2tdrObQpgfxCFxFnjyX9/Kt4YNVhnHuO8LdJfbl2TDJGg/yBLZqHhDkhhBChIP//CNG8JNCdgKDCJ5Zo5ufa6fDFNvYfiCOsZ3dsD93JvVk2fvwhnxHJcTxxaSrJ8eGhbrYQQgghhBCijZFAdwxLdi3hhbUvsL90Px3DO3JN/2v4NutbVuauZFJ4Cre8l07pFgMOeywd7r2dz08Zw5yvfkFTzkMX9efKkd0xSK+cEEIIIY4iMzOTCy64gE2bNjX6ub///nuefvppPvvsMxYvXsyWLVuYOXPmCZ0rKSmJyMhIjEYjJpOJ9PT0Rm6tEOK3kqocR7Fk1xJm/zSb3NJcNJrc0lweX/U46/al88LOnlz38BpKNxuJmXwulvcXc4ujJ/d/upXB3WL56v/GcfWoJAlzolX4eF0OY+Z8S/LMJYyZ8y0fr8tp8muef/75FBYWUlhYyLx586r2f//991xwwQVNfv3favbs2XTp0oVBgwYxaNAgPv/889A0JOMDeG4AzI7xfc/4oMkv2dpeqw8//JD+/ftjMBhq/bH5+OOP07NnT3r37s1XX30VkvYt2bWECQsmkPpmKhMWTGDJriVNfs3W9hoe7fetJbyGAK68PDKvvAp3K5srNnny5BMOc5W+++471q9fL2FOiBZCAt1RvLD2Bco95cSUaGa/7Sa6RNM7S/PYG046fbANS0IU3d5/iyUX3MZ5b21ky75inrg0hf/8aQRd4+yhbr4Qx+XjdTnMWriRnEIHGsgpdDBr4cYmD3Wff/45MTExtf7AbE5ut/s3HX/77bezfv161q9fz/nnn99ErTqKjA/g09ugKAvQvu+f3tbkoa61vVYDBgxg4cKFjBs3Lmj/li1beP/999m8eTNffvklt9xyCx6Pp7GbelR1fVA4+6fZTR7qWttrCHX/vrWE17DSwXkv41izhvx5LzfaOd1uN9OmTSM1NZWpU6dSVlbGQw89xPDhwxkwYAA33ngjWmsAXnzxRfr160dqaiqXX345AKWlpVx33XUMHz6cwYMH88knn9S6xr///W9uvfVWAK655hpuu+02Ro8eTY8ePViwYEHVcU899RTDhw8nNTWVBx98sNGeoxCi8cmQy6PYX7ofgEuXeemTBX9/20Pnw3AwCrrccAYH/vg4Vy7czLq9WzmrTwcevTiFjtHWELdaiGB//3QzW/YV13v/ur2FOD3eoH0Ol4e/LsjgvVV763xMv85RPHhh/6Ne98knn8RqtXLbbbdx++23s2HDBr799lu++eYb3njjDZYtW0Z6ejozZ85k586dDBo0iHPOOYdJkyZRUlLC1KlT2bRpE0OHDuXtt9+ud5J9UlIS06ZN49NPP8XlcvHhhx/Sp08fCgoKuO6669i1axd2u51XX32V1NRUZs+ezb59+8jMzCQ+Pp4JEybw8ccf4/F42LRpE3feeSdOp5P//Oc/hIWF8fnnnxMXF3eMn3Ij+WIm7N9Y//3Zq8FTEbzP5YBPboU1b9b9mI4pcN6co162rb1Wffv2rfP6n3zyCZdffjlhYWEkJyfTs2dPVq1axahRo4768/ktnlj1BNsKttV7f0Z+Bk6vM2hfuaecB5Y/wIJfFtT5mD5xfbhnxD1HvW5bew3r0xyv4f7HHqNia/2vIYB2OnFkZIDWFL7/PhVbt6KOsuZaWN8+dLz33mNee/v27bz++uuMGTOG6667jnnz5nHrrbfywAMPAHDVVVfx2WefceGFFzJnzhx2795NWFgYhYWFADz66KOMHz+e+fPnU1hYyIgRIzj77LOPes3c3FyWLVvGtm3bmDx5MlOnTmXp0qXs2LGDVatWobVm8uTJ/PDDD4wbNw6lFBMmTEApxU033cSNN954zOclhGhaDeqhU0qdq5TarpT6VSlVq/9eKTVOKbVWKeVWSk1tyLVCoaM5ipgSzfgMjQHodBgWD4c5N0fwnyF3MukfK9h9sJTnfz+I16YNkzAnWqWaYe5Y+4/XuHHj+PHHHwFIT0+npKQEl8vFsmXLGDt2bNVxc+bM4ZRTTmH9+vU89dRTAKxbt47nn3+eLVu2sGvXLpYvX37Ua8XHx7N27VqmT5/O008/DcCDDz7I4MGDycjI4LHHHuPqq6+uOn7NmjV88sknvPvuuwBs2rSJd999l1WrVnHfffdht9tZt24do0aN4q233qp63EsvvURqairXXXcdhw8fbtDP54TUDHPH2n+c2uJrVZecnBy6du1atZ2YmEhOTtMPLw5UM8wda//xaouvYV2/by3hNQRw7tsXvN1IbejatStjxowB4Morr2TZsmV89913jBw5kpSUFL799ls2b94MQGpqKn/84x95++23MZl8n88vXbqUOXPmMGjQIM444wzKy8vZu7fuD+YqTZkyBYPBQL9+/Thw4EDVeZYuXcrgwYMZMmQI27ZtY8eOHQAsX76ctWvX8sUXXzB37lx++OGHRnnuQogTd8I9dEopIzAXOAfIBlYrpRZrrbcEHLYXuAa4qyGNDJUZhwvZ/WP1UA63Aexu+GNBMff+sp3zUzry98kDaB8ZFsJWCnF0x+pJGzPnW3IKHbX2d4mx8d+bTvxT76FDh7JmzRqOHDlCWFgYQ4YMIT09nR9//JEXX3yRxx9/vN7HjhgxgsTERAAGDRpEZmYmp512Wr3HX3LJJVXXXLhwIQDLli3jo48+AmD8+PEcOnSIoqIiwDeHxGazVT3+zDPPJDIyksjISKKjo7nwwgsBSElJISMjA4Dp06dz//33o5Ti/vvv584772T+/Pkn+uOp2zF60nhugH+4ZQ3RXeHaEx+y19Zeq/pUDlUL1Njl1Y/VkzZhwQRyS3Nr7e8U3ok3zn3jhK/b1l7D+n7fmuM1PFZPmisvj53nTIDKtmiNt7iYLs8+g6l9+wZdu+ZzUUpxyy23kJ6eTteuXZk9ezbl5eUALFmyhB9++IHFixfz8MMPs3nzZrTWfPTRR/Tu3TvoPJVBrS5hYdV/w1T+fLXWzJo1i5tuuqnW8Z07dwagQ4cOXHzxxaxatarW8GYhRPNqSA/dCOBXrfUurbUTeB+4KPAArXWm1joDaNhH/SEyYe8+ztyoMfszndkL4zM0U/PymffHIcz741AJc6LVu3tib2xmY9A+m9nI3RN71/OI42M2m0lKSuKNN95g9OjRjB07lu+++46dO3fWOySuUuAfGEaj8ZhzbyqPDzz2aH/4hYcHLyMSeD2DwVC1bTAYqs6XkJCA0WjEYDBwww03sGrVqqO2qUmc9QCYbcH7zDbf/gZoa69VfRITE8nKqg7E2dnZVX+cNpcZQ2ZgNQaP5rAarcwYMqNB521rr2F9v28t4TU8OO9ltDf4zxrt9TbKXLq9e/eyYsUKAN57772qYB0fH09JSUnVHDev10tWVhZnnnkmTz75JIWFhZSUlDBx4kT+8Y9/VL0e69atO6F2TJw4kfnz51NSUgL4ekbz8vIoLS3lyJEjgG++3tKlSxkwYECDnrMQouEaEui6AIEfFWf79/1mSqkblVLpSqn0/BZULWrv5ngMNf6PMmjI2hLP+SmdQtMoIRrZlMFdePySFLrE2FD4euYevySFKYNP6Nc5yLhx43j66acZN24cY8eO5ZVXXmHQoEFBn0JHRkZW/YHQmMaNG8c777wD+Cr5xcfHExUVdcLny82t7lVZtGhRaP6ISb0MLnzR1yOH8n2/8EXf/gZqS69VfSZPnsz7779PRUUFu3fvZseOHYwYMaLRr3M0k3pMYvbo2XQK74RC0Sm8E7NHz2ZSj0kNPndbeg3r+31rCa+hY/16cLmCd7pcOE4wPAXq27cvb775JqmpqRQUFDB9+nRuuOEGUlJSmDJlCsOHDwfA4/Fw5ZVXkpKSwuDBg7n99tuJiYnh/vvvx+VykZqayoABA7j//vtPqB0TJkzgiiuuYNSoUaSkpDB16lSOHDnCgQMHOO200xg4cCAjRoxg0qRJnHvuuQ1+3kKIhmlIUZS6xjjU/ojuOGitXwVeBRg2bNgJnaMp7MuPJd5bo5iEV7EvP5ZTQtMkIZrElMFdGiXA1TR27FgeffRRRo0aRXh4OFarNWg+D0C7du0YM2YMAwYM4LzzzmPSpIb/YQu+sufXXnstqamp2O123nyznqIhx+mvf/0r69evRylFUlIS//znPxulnb9Z6mWNEuBqakuv1aJFi/jLX/5Cfn4+kyZNYtCgQXz11Vf079+fyy67jH79+mEymZg7dy5Go/HYJ2xkk3pMapQAV1Nbeg3r+31rCa9hj48XNcl5k5KS2LJlS639jzzyCI888kit/cuWLau1z2az1flv0xlnnMEZZ5wB+CpbXnPNNYCv4mWgyh45gBkzZjBjRu2e4w0bNhztaQghQkDVNUziuB6o1ChgttZ6on97FoDWutZAfaXUv4HPtNZ1l/AKMGzYMN1S1jUZM+dbhhZ/zV9NH9BZHWKfbseT7stYE3UOy2eOD3XzhKjX1q1bjznMSgghhGgq8v+QEA2jlFqjtR52PMc2pIduNdBLKZUM5ACXA1c04Hwtzt0TezNroZPFzurJ4TazkccbOLdICCGEEEIIIRrDCQc6rbVbKXUr8BVgBOZrrTcrpR4C0rXWi5VSw4FFQCxwoVLq71rro5fca0Eqh6A99dV29hU66Bxj4+6JvZtkaJoQ4uguvvhidu/eHbTviSeeYOLEiSFqkaiPvFatn7yGQgjRepzwkMum0pKGXArRWm3dupU+ffo0ejlvIYQQ4li01mzbtk2GXArRAL9lyGWDFhYXQrRMVquVQ4cO1VlKXAghhGgqWmsOHTqE1Wo99sFCiEbRkDl0QogWKjExkezsbFrSMiBCCCFODlartWqxeiFE05NAJ0QbZDabSU5ODnUzhBBCCCFEE5Mhl0IIIYQQQgjRSkmgE0IIIYQQQohWSgKdEEIIIYQQQrRSLW7ZAqVUPrAn1O2oQzxwMNSNEG2avMdEU5L3l2hK8v4STUneX6IptdT3V3etdfvjObDFBbqWSimVfrxrQQhxIuQ9JpqSvL9EU5L3l2hK8v4STaktvL9kyKUQQgghhBBCtFIS6IQQQgghhBCilZJAd/xeDXUDRJsn7zHRlOT9JZqSvL9EU5L3l2hKrf79JXPohBBCCCGEEKKVkh46IYQQQgghhGilJNAJIYQQQgghRCslge44KKXOVUptV0r9qpSaGer2iLZDKdVVKfWdUmqrUmqzUmpGqNsk2h6llFEptU4p9Vmo2yLaHqVUjFJqgVJqm//fslGhbpNoO5RSt/v/f9yklHpPKWUNdZtE66WUmq+UylNKbQrYF6eU+loptcP/PTaUbTwREuiOQSllBOYC5wH9gD8opfqFtlWiDXEDd2qt+wJpwJ/l/SWawAxga6gbIdqsF4AvtdZ9gIHIe000EqVUF+A2YJjWegBgBC4PbatEK/dv4Nwa+2YC32itewHf+LdbFQl0xzYC+FVrvUtr7QTeBy4KcZtEG6G1ztVar/XfPoLvD6EuoW2VaEuUUonAJOC1ULdFtD1KqShgHPA6gNbaqbUuDG2rRBtjAmxKKRNgB/aFuD2iFdNa/wAU1Nh9EfCm//abwJRmbVQjkEB3bF2ArIDtbOQPbtEElFJJwGBgZWhbItqY54G/At5QN0S0ST2AfOAN/7De15RS4aFulGgbtNY5wNPAXiAXKNJaLw1tq0QblKC1zgXfB+1AhxC35zeTQHdsqo59staDaFRKqQjgI+D/tNbFoW6PaBuUUhcAeVrrNaFui2izTMAQ4GWt9WCglFY4XEm0TP65TBcByUBnIFwpdWVoWyVEyyOB7tiyga4B24lId79oREopM74w947WemGo2yPalDHAZKVUJr7h4uOVUm+HtkmijckGsrXWlSNEI63wAAAgAElEQVQLFuALeEI0hrOB3VrrfK21C1gIjA5xm0Tbc0Ap1QnA/z0vxO35zSTQHdtqoJdSKlkpZcE3GXdxiNsk2gillMI392Sr1vrZULdHtC1a61la60StdRK+f7u+1VrLp9ui0Wit9wNZSqne/l1nAVtC2CTRtuwF0pRSdv//l2chRXdE41sMTPPfngZ8EsK2nBBTqBvQ0mmt3UqpW4Gv8FVXmq+13hziZom2YwxwFbBRKbXev+9erfXnIWyTEEL8Fn8B3vF/6LkLuDbE7RFthNZ6pVJqAbAWX1XodcCroW2VaM2UUu8BZwDxSqls4EFgDvCBUupP+D5E+F3oWnhilNYyHUwIIYQQQgghWiMZcimEEEIIIYQQrZQEOiGEEEIIIYRopSTQCSGEEEIIIUQrJYFOCCGEEEIIIVopCXRCCCGEEEII0UpJoBNCCNFmKaU8Sqn1AV8zG/HcSUqpTY11PiGEEOJEyDp0Qggh2jKH1npQqBshhBBCNBXpoRNCCHHSUUplKqWeUEqt8n/19O/vrpT6RimV4f/ezb8/QSm1SCm1wf812n8qo1LqX0qpzUqppUopW8ielBBCiJOSBDohhBBtma3GkMvfB9xXrLUeAbwEPO/f9xLwltY6FXgHeNG//0Xg/2mtBwJDgM3+/b2AuVrr/kAhcGkTPx8hhBAiiNJah7oNQgghRJNQSpVorSPq2J8JjNda71JKmYH9Wut2SqmDQCettcu/P1drHa+UygcStdYVAedIAr7WWvfyb98DmLXWjzT9MxNCCCF8pIdOCCHEyUrXc7u+Y+pSEXDbg8xNF0II0cwk0AkhxP9n777jo7rvfP+/vlM06l0CSajQJcCYasCOsY2EjY0xgtjpdeM42c1eZ3ezyW7ub7Mh2ZJssncTZ3fvTZxenOKNQTbGsY1km2KK6TYgAaKoo941fb6/P87RaNRAgFCBz/Px0GNmzjkz5zujQcx7Pt8iblcfDLncb17fB3zIvP5RYK95vQT4cwCllFUpFTtWjRRCCCGuRL5JFEIIcSuLUEodD7n9qta6d+kCh1LqIMaXmx82tz0N/Ewp9WWgEfi0uf2LwLNKqc9gVOL+HKi76a0XQgghrkLG0AkhhLjtmGPolmmtm8a7LUIIIcSNkC6XQgghhBBCCDFJSYVOCCGEEEIIISYpqdAJIYQQQgghxCQlgU4IIYQQQgghJikJdEIIcZMopXKUUlopZTNv/0kp9cmRHHsd5/rfSqmf3Eh7b3dKKYdS6rRSauo4nPsXSqlbZkFypdRbSqknzesfVUq9fhPOMSrveaXUQqXUvtFokxBCjAcJdEIIMQyl1GtKqW8OsX2jUurytYYvrfXDWutfjkK77ldKVQ947H/VWj95o499m3sK2K21vgzBkOVRSnUppTqVUkeUUveN9MGUUpeUUgWj3ciQ8L9jwPbfKKW2jPb5bpTW+jmt9YM38hg38z2vtX4XaFNKbbjRxxJCiPEggU4IIYb3C+DjSik1YPvHgee01r6xb9Lt5Xorltfpc8CvB2z7jtY6GogD/h+wVSllHcM2XclKpdQ9N/ogY/waT1TPYfz+hRBi0pFAJ4QQwysCEoF7ezcopRKAR4FfmbfXK6WOKaU6lFJVV6qQDOiGZlVK/btSqkkpdQFYP+DYTyulSs3K0AWl1OfM7VHAn4B0s3LUpZRKV0ptUUr9JuT+jymlTiml2szz5oXsu6SU+lul1LtKqXal1B+UUuHDtHmmUuoNpVSz2dbnlFLxIfszlVJblVKN5jH/FbLvsyHP4bRSaom5XSulZoUcF+xu2FuJUUr9nVLqMvBzpVSCUupl8xyt5vVpIfdPVEr9XClVa+4vMrefDK26KKXs5nNYNMTzzAJmAgeHeh201gHgtxjvhylXe22UUr8GsoDt5u/oK+b29yml9pm/lyql1KdCTpOglNphvl4HlVIzh2pLiO8Aw3bTNF//cqVUi1LqJaVUesg+rZT6glLqHHAuZNtfKKXOmW34J/M57jff388rpcLMY6/4OxnQjk8ppfaa178S8r7tUkp5lVK/MPeN53v+LSBfKeW4ymsuhBATjgQ6IYQYhtbaCTwPfCJk8weAMq31CfN2t7k/HiOU/blSqnAED/9ZjGC4GFgGPD5gf4O5Pxb4NPA9pdQSrXU38DBQq7WONn9qQ++olJoD/A74KyAFeAUjWIQNeB7rgOnAQuBTw7RTAd8C0oE8IBPYYp7HCrwMVAA5QAbwe3PfE+ZxnzCfw2NA8wheF4CpGMEpG6MbpAX4uXk7C3AC/xVy/K+BSGA+kAp8z9z+K+BjIcc9AtRprY8Pcc47gAvDVV3N5/oJ4CJQ37uZYV4brfXHgUpgg/k7+o4ZGv8E/CfG72URENqWDwPfABKAcuBfhmpLiP8G5qghunUqpdaYbfsAkIbxO/r9gMMKgRXAvJBt64ClwErgK8CzwEfN57bAbCNc/XcyJK31d3rftxivWSPGvzEYx/e81roG8AJzr/YchBBiopFAJ4QQV/ZL4AmlVIR5+xPmNgC01m9prd/TWgfMsTi/A0YyzuoDwPe11lVa6xaMD99BWusdWuvz2rALeJ2QSuFVfBDYobXeqbX2Av8ORAB3hxzzA611rXnu7RjhYhCtdbn5OG6tdSPwHyHP7y6MMPNlrXW31tqltd5r7nsSo7viIfM5lGutK0bY/gDwdfOcTq11s9b6Ba11j9a6EyPo3AeglErD+LD/ea11q9baa75eAL8BHlFKxZq3P87gLpW94oHOIbb/rVKqDSO4fx/4mtbaP4LXZigfBYq11r8z29k8IFxu1Vq/Y4bK5xjmdxLChfFaDFWl+yjwM631Ua21G/gqsEoplRNyzLe01i3mFxe9/k1r3aG1PgWcBF7XWl/QWrdjhNHF5nMf9ncyEua/pyLgGa31K+Zjjvd7vhPjfSCEEJOKBDohhLgCM6A0AhuVUjOA5Rhd7wBQSq1QSr1pdj1rBz4PJI/godOBqpDb/cKOUuphpdQBs7tcG0Z1aSSP2/vYwcczuwtWYVTQel0Oud4DRA/1QEqpVKXU75VSNUqpDoyQ1NuOTKBimKpWJnB+hO0dqFFr7QppQ6RS6kdKqQqzDbuBeLNqlgm0aK1bBz6IWcV5G3i/2RXyYYygNJRWIGaI7f+utY7HCAfLgO8qpR4223Wl12YoV3tNRvQ7GeDHwBQ1eEKPge+BLowKaeh7oIrB6kOuO4e4HQ1X/Z2MxE+BM1rrf+vdMAHe8zFA2wjPJ4QQE4YEOiGEuLpfYVTmPo5RsQj9kPtb4CUgU2sdB/wQoyve1dRhfMDvldV7xRzH8wJGlWGKGSheCXlcfZXHrsXoCtf7eMo8V80I2jXQt8zzLdRax2J0YextRxWQpYaeVKMKY0zaUHowukj2GrhMwMDn9yWMrnArzDasNrcr8zyJKmRc3wC/NNv8BLDf7Fo3lHeBGcM8F8yq0UmMgNg73vFKr81Qz+NKr8l1MatR3wD+acC5B74HooAk+r8HrvY+upIr/U6uSCn19+Z9PxOybVzf8+b4wjDgzEiOF0KIiUQCnRBCXN2vgAKMcW8Dlx2IwagQuZRSdwEfGeFjPg88rZSapoyJVv4+ZF8Y4MCoDPrMilDotO/1QJJSKu4Kj71eKZWvlLJjfPh2A9ez1lYM0IUxrXsG8OWQfe9gBNNvK6WilFLhqm/WxZ9gdFdcqgyzlFK9H7iPAx9RxsQw67h6V70YjOpQm1IqEfh67w6tdR1GV8D/a07UYVdKrQ65bxGwBPgi5kQ2Q9FaV2NMDnLXcMcopXKB9wGnQto13GsDxu9pRsjt54ACpdQHlFI2pVSSGmKCluvwa4z3y7qQbb8FPq2UWmSGpX8FDmqtL43C+eAKv5MrMd/LTwOFA7p6jvd7/n7gDbN7qhBCTCoS6IQQ4irMD8H7gCiMalyovwC+qZTqBP6RvgkerubHwGvACeAosDXkfJ0YH3qfx+gK+JHQ82qtyzDG6l0wZ/RLD3lctNZnMKpF/wk0ARswJufwjLBtob6BEYjagR0D2uk3H3sWxgQg1RhjmdBa/w/GuKrfYoxN6p0xFIxwtQGje9tHzX1X8n2MLo9NwAHg1QH7P44xoUUZxsQafxXSRidG5Wd6aNuH8SPzsUL1zsrYjTGm6+fmcXCF18b0LeAfzN/R32qtKzG6EX4JaMEItndepU1XZf4evk7f64vWugT4GsZzr8OoDH7oRs8V4mq/k+F8EGPSklLVN2PlDyfAe/6jGNV1IYSYdJTWN9LjQgghhJjYlFL/CMzRWn/sKsc5gGNAvln5E7cBpdQdwLNa61Xj3RYhhLgeEuiEEELcsszugMeAj2utd493e4QQQojRJl0uhRBC3JKUUp/FmIjkTxLmhBBC3KqkQieEEEIIIYQQk5RU6IQQQgghhBBikhpyvZ3xlJycrHNycsa7GUIIIYQQQggxLo4cOdKktU4ZybETLtDl5ORw+PDh8W6GEEIIIYQQQowLpVTFSI+VLpdCCCGEEEIIMUlJoBNCCCGEEEKISUoCnRBCCCGEEEJMUhLohBBCCCGEEGKSkkAnhBBCCCGEEJOUBDohhBBCCCGEmKQk0AkhhBBCiAlnx4UdPPjHB1n4y4U8+McH2XFhx3g3SYgJacKtQyeEEEIIIW5vOy7sYMu+Lbj8LgDquuvYsm8LAOtnrB/Hlgkx8UiFTgghhBBCTCjPHH0mGOZ6ufwunjn6zDi1SIiJSwKdEEIIIYSYEJqcTfz85M+p664bcn9ddx0/ee8nVHRUjHHLhJi4lNZ6vNvQz7Jly/Thw4fHuxlCCCGEEGIMeP1edlXvoqi8iL01e/FrP3aLHW/AO+jY0O2z4mexNnstBdkFzI6fjVJqrJsuxE2jlDqitV42omMl0AkhhBBCiLF2puUMReVF7Liwg1Z3KykRKWyYuYGNszZS2lzabwwdQLg1nC13b2FJ6hJKKkvYWbGTYw3H0GiyY7PJz8pnbfZa5ifNl3AnJj0JdEIIIYQQYsJpc7Wx4+IOXix/kdKWUuwWOw9kPkDhrEJWpa/CZumbr2/HhR08c/QZLndfZmrUVL645IuDJkRpcjbxRuUbFFcUc+jyIXzax9SoqRRkFVCQXcCilEVYLdaxfppC3LBRD3RKqXXAM4AV+InW+tsD9n8e+ALgB7qAp7TWp5VSOUApcMY89IDW+vNXOpcEOiGEEEKIW4cv4GNf7T6Kyot4q+otvAEveYl5FM4q5JHpjxAfHj8q52l3t/NW1VsUVxSzr3YfnoCHpPAk8rPyyc/OZ/nU5dgt9lE5lxA326gGOqWUFTgLrAWqgUPAh7XWp0OOidVad5jXHwP+Qmu9zgx0L2utF4y08RLohBBCCCEmv4vtFykqL2L7+e00OhtJcCSwfsZ6CmcVMjdx7k09d7e3mz3Ve9hZsZM9NXtw+pzEOeK4f9r9FGQXsCp9FQ6r46a2QYgbcS2BbiTr0N0FlGutL5gP/ntgIxAMdL1hzhQFTKx+nEIIIYQQ4qbr8nTx6qVXKSov4kTjCazKyr0Z91I4q5DV01Zjt45NhSzKHsW66etYN30dLp+LfbX7KK4o5o3KN3jx/ItE2iK5b9p95Gfnc2/GvUTaI8ekXULcDCMJdBlAVcjtamDFwIOUUl8A/gYIA9aE7JqulDoGdAD/oLXeM8R9nwKeAsjKyhpx44UQQgghxPgK6ACHLh+iqLyI4opiXH4XM+Nm8qWlX+LRmY+SHJE8ru0Lt4WzJmsNa7LW4PV7eefyO+ys2MmbVW/yp0t/wmF1cE/6PRRkF3Bf5n3EhsWOa3uFuFYj6XL5BPCQ1vpJ8/bHgbu01v9rmOM/Yh7/SaWUA4jWWjcrpZYCRcD8ARW9fqTLpRBCCCHExFfdWc2L51/kpfKXqO2uJcYew8PTH6ZwViELkhdM+Jkm/QE/RxuOUlxRTHFlMQ09DdgsNlakrWBt1loeyHqAxPDE8W6muE2N9hi6VcAWrfVD5u2vAmitvzXM8RagVWsdN8S+t4C/1VoPm9gk0AkhhBBCTEw93h6KK4spKi/i0OVDKBQr01ZSOKuQNVlrCLeFj3cTr0tABzjZdJLiimJ2Vuykuqsai7KwdMpSCrIKyM/KZ0rUlPFupriNjHags2FMipIP1GBMivIRrfWpkGNma63Pmdc3AF/XWi9TSqUALVprv1JqBrAHuENr3TLc+STQCSGEEEJMHFprjjcep6i8iNcuvUa3t5vMmEw2ztzIYzMfIy06bbybOKq01pxpPWNU7iqKOd9+HoCFKQtZm2UsZD4tZto4t1Lc6m7GsgWPAN/HWLbgZ1rrf1FKfRM4rLV+SSn1DFAAeIFW4C+11qeUUu8Hvgn4MJY0+LrWevuVziWBTgghhBBi/NV317P9wnZeLH+RSx2XiLBF8GD2gxTOKmTplKUTvkvlaLnQfoGSCmMh89KWUgDyEvOCC5nPiJ8xzi0UtyJZWFwIIYQQQlwzj9/DG1VvUFRexP7a/QR0gKVTlrJx5kYeynnotp8NsrqzmpLKEoorijneeByA6XHTKcgqYG32WnITc8c16HobGqj5my8x7Xv/gS0lZdzaMSm8+zyUfBPaqyFuGuT/Iyz8wHi3KkgCnRBCCCGEGBGtNadbTlN0rohXLr5Ch6eDqVFTeWzmY2ycuZGsWJmBfCgNPQ2UVJZQUlHCofpDBHSAjOgMCrIKKMguYGHKQizKMqZtqtvyDdr+8AfiP/Qh0r7+j2N67knl3edh+9PgdfZts0fAhh9MmFAngU4IIYQQYqKZYBWBFlcLL59/maLzRZxrPUeYJYz87HwKZxWyYuoKrBbruLVtsml1tfJm1ZvsrNjJgboD+AI+UiNTyc/KpyCrgCVTlmCzjGS1sGujtcbf1IS3thZXaSmX/+mfwe9HhYUxq6RYqnTD+d4CaK8avD0uE/765Ni3ZwgS6IQQQghx0+24sINnjj7D5e7LTI2ayheXfJH1M9aPd7MmpglSEfAGvOyt3ktReRG7q3fj0z7uSL6DwlmFPJTzEHGOQZOUi2vU6elkV/UuiiuKebvmbVx+FwmOBNZkrSE/K5+VaStHvMC69vvxNTTgra3FW1PTd1lTa1yvrUV7PEPe15qURMrTTxP7yMNYY2JG8ylOXj4PXHgLfvvEMAco2NI2li0algQ6IYQQQtxUOy7sYMu+Lbj8ruC2cGs4W+7ecuuFOq3B5wZvD3i6jR+veenpAU+XuS/0eu9x5vXzb4LfPfixx6giUN5aTlF5EdsvbKfF1UJSeBIbZm5g48yNzEqYddPPf7vq8fbwdu3b7KzYye7q3XR7u4mxx3Bf5n0UZBWwKnU5tqb2/iGtN7jV1uK9fBl8vn6PaU1Kwp6ejj0jw7hMT8cSHcXlf/x6/3CnFGiNcjiIWbuW+M2biFy5EmUZ226g4643xJ0ugrKXwdUOKGCIDDRJK3SjX/sVQgghxC2tsaeRb7/z7X5hDsDld/HPB/4Zb8BLTmwOWbFZJDgSxm6SiEDADFojDFlD3faax3t6+m/X/pG3Q1khLBrCIsEeCWFRQ4c5MLp97foO5D0Gqbmj8zr0PrS7nVcvvkpReREnm09iUzbuy7yPwlmF3JNxD3bLyKpE4vqF+y2s9s9ilYrApRZxqeIwjedP4q19mbC2Ii52Qr94pRS21FTs6elELFpEbGhwy0jHnpaGJSJi0HnqtnyDQUUam43oNQ9gS0ykY8crdLz8Mrb0NOILC4nbtImwzMyb+tzHlc8DF3fBqSIo226EOEcc5K6H+YXQ04xv+19jC/kb5rOGY8ufnOMOpUInhBBCiCvq8fZwuP4w+2v3c6DuAOVt5SO+b4w9hqzYLLJis8iOzSYrJovsqHSyw5OIU7brDFnm9WClzLzuc169QaFs4UbYskcZ4SssygxgoWEsOmRflHEZFtn/elh0X3ALiwJrmFEdCTXcmB1rGPjNqkryHCPY5W2AtDsHP8YI+AN+DtYdpKi8iJLKEjwBD3MS5lA4q5D1M9aTGJ54zY8phufv6sZbW9O/qhbSHdLf1NT/DlYr9qlTsWWk054QRnl4B4dVBRcjumhLsDNn7t2smfkgD2Q+cE3dXy8UbsJdVjZouyM3lxlF2wi43XQWF9O+dRvd+/aB1kQuX07c5s3EPvQglshbYPZSvxcu7IJT28xKXBs4Yo0QN68QZj4ANgcARcdq2Lvt//JX/J501UytTuL7fIj3bfoLChdnjPMTMUiXSyGEEEJcO78PfE58nh5ONp7gQP1hDjQe40TbOXzaj8NiY0l0Diujs/l11U6arIO7bqX5/Pw4agEV3g4qfV1UBFxU4qFCBaizQCAkpMT5/WR7fWT5fGR5veR4jctsr4/o3s8nyjIgcIUGqYFhbISBq/f6WE76caUxdDn3Gh9AS1+CS28b1cD4bCPY5T0G05bDVbrJVXZUUlRexEvnX6K+p544RxyPTH+EwlmF5CXm3TZrxo0mrTWB9na8tbV4amrwmSHNY4Y3X00t/vb2fvdRYWHY09KMqlpG+qCukbbUVJStfwe5gA5wovEEOyt2UlJRQm13LVZlZfnU5azNXsuarDUkRySP2vPy1tXR/uKLtG3bhreiEktkJDEPryN+82YiliyZXO+V3hB3ehuUhoS4uY/A/E39Qlyoe779BvWBfThSXkPZ29DeeNyNDzHFcjdv//2acXgig0mgE0IIIa7HRJmF0O8Dn6vvx+sa4rbTHNflHP7YIe83+HG0z80l7eVAuJ39EeEciginy2JBaU2ex8NKp4tVTheL3B7Czc8NO6Ii2ZKciCskaIQHAmxpamF9ROaAIGUEKI8tnGoLVCovFQE3lf5uKrwdVLhbuezpPxFBoiOB7NgssmJzjMpeSIVv0q6FNpL3V3cznHnFCHfn34SAF2LSIPdRI+Bl3wNWIxB0e7t5/dLrFJUXcbThKBZl4e70uymcVcgDmQ8QZg0bhyc5eWit8Tc3D5hwpP8YtkB3d7/7qMhI7Olp/UJaWLBLZAbWpKQbGqPWu4REcUUxxRXFXOq4hEKxOHUxBdkFFGQVkBaddqNPPXgu55EjtG3dRserr6J7egjLziZu0ybiCjdinzp1VM4z6vzekO6UL4OzNSTEFcLMNUOGOIBOl5c955p4evtPCU/birJ4g/t0wI67bjPn/vc/jNUzuSIJdEIIIcS1Gq6C8vB3jQ8KwRBkBimfc4Rhyz1E+Bpun3k74Bu+nVejLGCLMD7Q2M3LIW4326wc1E72Bzo54G3hcsAYS5Jhi2ZlVDar4mZyV9xsEsITh36cX25gR6CdZxLiuWyzMtXn54utbay3JV3XpAIun4uqzioqOyqp6KwwLjuMywZnQ79jUyJS+gW83vF6mTGZhNvCr/+1m2hc7XD2NSPcnSs2wndkEodn3k1RRBg7W0/i9DnJic1h46yNbJixgSlRU8a71ROG9vvxNTYOnhkyJLBpd/+xjZbY2P5Vtd4qW7pRcbPGx49ZBUtrTXlbOcWVRrg723oWgPlJ8ynINhYyz47NHpVzBbq76Xh9J+1bt9Jz6BBYLETdfTfxmzcRnZ+PxTF0QBorfq8L94U3cJ9+EXf567jdHbjDonBPvwd39j24p8zHrTRuvxuXz4XH78HlNy4bujopb2zjUksblzs70fiwxZzqF+Z6KV8C735m9zg8w8Ek0AkhhBAj4emBprPQWAav/C24O0fxwZUZhMKNH3t43/Xg7asHr77bV3qckB+rfcixV06fkyP1RzhQe4D9dfuDHw5jw2JZkbaClWkrWZW2iszYEU6UMIbT8Pd4e6jqrOJSx6W+oNdpXLa4WvodOzVqKtkx/St62bHZTIuZNqkrVnWt53nxyH/yYu1eqrWbqECAdU4vhclLuHPBR1CzCoxK6C3G29BAzd98iWnf+49Ba6pprxdvff2gqlrw+uXL4O3/od2amDggsPUPbhN5ev/KjspguHuv6T0AZifMDi5kfrblLD849oMbXkbEU1lJe1ERbduK8NXVYYmLI279I8Rt2kzY/Dw8AQ9unxu3f4gfnzsYpIKXPheegHE51PEDt/ULZN4eXH43vqFmpBwhrRVoGxbshNvCiQ4Lp9FVO8zRivc++e51n2s0SaATQgghQnmdRnBrKIPGUmg8Aw2l0HqJIaeuHujh75jh6RqC1zDBaqz4A35ON5/mQJ0R4I43HMcb8GK32FmSuoSV6UaAy03Mvf4FpCdAF9VOTyeVnZX9Knq9Fb42d183TouykBaVRlaMMUFLb1UvOzab9Oj0CTnjo8vnoqSyhKLyIg7WHUSjWTF1BRtnrKfAZyPi7KtQ9go4W4yurbMKYN5GmP0ghMeOd/NHRe3Xvkb7H18gYtkyIpcu6TfpiK++3pjZtJdS2FJS+nWH7DeWLS3t1pj8A7jcfZmSyhJ2VuzkaP1RNBqFQof8PbNb7Dw+53HmJ80fUZAaGMjcXifZ5zpYeriDRaUuwnxQmQJvLrSwZ76iI+ra/76FW8MJs4b1Xdr6bjusDhwWOw53J46OOhxtVTi8TmNb0mwcU+7AkTofR1iUcazVEby/Dtg4XePk0MVO3jnfSWu3xqLsLM1KYW1uBgXzpjI9OSrYjgf/+CB13XWD2pcWlcbrj79+fb+UUSaBTgghxO3J64Smc0bFraHUuGwsM4KbNj/4WWyQNAtSciE1r+/yN5uNYDLQBFqX6Eq01lR1VgVnojx4+SCdHqPimJuYG6zALZ6ymAjb4GnPb0Xt7nYqOir6VfQqO4zw1+ntq8ZalZWM6IxBVb2s2CzSo9KvP/BeB6017zW9R1F5Ea9efJVObycZ0RlsnLmRx2Y9Rkb0gBn4/D6o2AunXzLGE3XVGzNnzngA5j1mdBeOnHwzW/o7Otj/zf9D4svP0xsbtFKEpaX1r6qFVNpsU6diCZu8Vdjr1eRsorCokHZP+9UPNjmsjv7BKiRg9Yal4I/NQZRLkXOoiszd53Q7yNgAACAASURBVIgtr0dbLXQum0P32hV4Vy4k3BGFw+YYfF/z/g6rgzBL2NDdVf0+uLS7b0xcT7Mx/nbuw8bslLPyjS/LBqhpc1JSWk9xaQMHzjfj8QeIDbdx/9xU8vNSuX9OKnGRQ39RMxnW0ZRAJ4QQ4tbmdZldJc8YFbfeytuQwW0upOQZa3yl5EHSTKN6NtAYdiEcLa2uVg7WHTSqcLX7qe02uhGlRaWxKn0VK9NWsiJthUxVP4DWmlZ3a7CqFxr4KjoqcIYsf2Cz2JgWPa1fRS8rNovsmGymRE3BokZnkeYmZxPbz2/nxfIXOd9+nnBrOGuz11I4q5BlU5eN7DyBAFS/Y4S70u3QXmmsiZfzPiPc5W6AmIk9xs7X1ETLL39Fw2+ew+rsIYCxTptXWSmZvoLp//LNCTOt/ESy8JcL+1XneikUOzbv6FfRslvsN/S+dZeX07ZtG+0vvYS/sQlrUhJxGzYQt2kT4XPnjOxB/D64tMdY7Lt0e1+Im7POmJ1yiBAXCGhOVLdRUtpAcWk9ZZeNL2WmJ0eRn5tKft4UluUkYB9i9t2h7Liwg2eOPnPDXVRvFgl0Qgghbg1eFzSfMwNbWV/lrfVi/+CWOLMvsKXMNSpuiTPBdo3f1k+ALoRX4vK5ONpwlAN1BzhQe4DSllLAWOvtrrS7jCpc+iqyYrIm19TjE4jWmiZn06CqXkVnBVUdVf2+0XdYHWTGZPar6PVW+FIjUwf9DgZ+gPzCoi8QZY+iqLyIvTV78Ws/i1IWUTirkIdyHiI6LPpGngjUHTfD3UvQXA4oyFxhhLu8DRCfdf2PP8q8dXU0//RntP3P/6A9Ho5mLGBB7WnCAn0Lurstdv7+8W/w6jc3jWNLJ6bx6EKofT669uyhfes2Ot96C7xewufPJ27zJuLWr8caH9//Dr3V5FPb+kKcPcqoxM0vNLoMDwhxPR4fe841UVJazxtljTR1ubFaFEuzEyjIM0LczJQb+HcygUmgE0IIMbn43EN3lWy50BfclNWoroV2lUzJNapw1xrcJomADlDaUhrsRnms/hiegAebxcailEXBKty8pHnYLLarP6C4IQEdoKGnIRjwKtorguP1qjqr8Ab6JuCIsEWQGZNJdmw22bHZtLpa2X5+O56AZ9DjpkaksmHmBjbO2sj0uOmj33CtjX9PvZW7emNCDdIWmeFuIyTPGv3zjoDn0iWanv0x7S+9ZMzquGg1v5t5P0veeZUHK94hTPcFOo+y8lr2Cvas/zR3ZsZx57R4FmXGMzMlGovl9v4CY7y7EPpaW+nYvp22rdtwl5Wh7HaiC/KJ37iRqIwAqsx87/U0mSFundGdcvbaQSGurt1JcWkDJaX17DvfjMcXICbcxn1zUijIm8L9c1OIj7w1/+aHkkAnhBBiYgoNbqHhbcjgNkRXyWHWFrqVVHdWs79uPwdqjXFw7W5jXMzshNmsSjMC3NIpSyfvWmy3KH/Az+Wey30VPbPCV9lRSXVnNT499FIUieGJlDxRMraBvPm88eG69CWoOWJsS53Xt5D5lPk3dUIfnz9A6dtHafvxj0k8vAefxcarWXfxx9n3405MYVlOIo//8KtMb6sZdN+L8Rk89+Q/825VO51u4zWNdthYOC2OOzPjgyFvatwttHzFCE2ULoSuk+/R9qv/R0fxXvw9XmwRfuJmeonLX4Hjvo8YlbiQGVkDAc17Ne3B8XCn6zoAyE6KJD93CgV5qSyfnjjirpS3Cgl0QgghxpfPbXTxaijtP86t5QL0fuOurJA4wwxsuX2Vt6RZt0Vw69Xubu83Dq66y5iYJTUy1Qhw6StZmbaS5IjkcW6puF6+gI8lv14y7Bind8dzmvT2aih92Qh3FfsAbfy7zNtgVO4yltxwuOt2+zhe1cahSy3U7jvEgre2sbz2FD02B7vyVtO0bjPzF0xnWXYis1ONalvRsRq+uvU9nN6+Cl2E3cq3Nt9B4eIMAgHNhaZujle1caKqjRPVbZTWdeD1G6/x1Nhwo4qXGc+iafHcMS2OmPCJN5PpLSPgh4q3+7pTdjcSsETS5V9O2zkL3cfPQSBAxJIlxG/ehD3/QfbVGZOalJQ10NjpxqJgaXYC+XlGiJuZEn1bdx2XQCeEEGJs+DxGcAtOTGL+NJ8PCW4W4wPiwK6SybNvq+DWy+13c7zheLAb5enm02g0UfYolk9dHgxx02On39YfZm41k2GadLoaoGyHEe4u7jYWuI+dZoa7DZC1EkYw42dDh4vDFa0cutTC4UutnK5t546Gc3zwbAmLGstxR0bT9ejjTP/sp8jIHH6SlqJjNXz3tTPUtjlJj4/gyw/NveKEKC6vn9N1HUbAq2rjRHU7F5u6ASOTzkyJZlFmfDDkzZ0aQ5jt9qr6jKpgiCsy3jPdjcbyGXMeMrtTPhisxHnrG6h5/gVat24lvK4alzWMvel3sGfWSlLuWUn+vKncPzeVxKhbvyvlSEmgE0IIMbp8Hmg539dFsrfy1nLe+NAH/YNbaHhLmmUsfn2bCugAZ1vPBgPc0fqjuPwubMrGwpSFwfXgFiQvkHFwt7DxHuN0zZytcOZV44N6eQn43RCVArmPGuFu+mqw2tFac76xm8OXWjh0qZXDFS1UNPcAEG5TfNBbybrjfyK+4iyWlBSS/+zPSPjAE1iioq7SgNHR1uPh3er2YCXveFUbzd3GOMYwm4X56bEsyjS6ad45LZ7spEj5IuVKAn6jknu6yBiT2d1ghLjZDxqzU85eC2HG71ZrzcmaDopL6ykpq+dkTQdozWp/PR9oOM709/ZhcfZgnzaNuE2FxBcWYs+QGUx7SaATQty6JvgshJOe32tW3Mr6lgJoKBsc3BKmm4EtZJxb0uzbOriFquuq6zcOrsXVAsCs+FnBmSiXTllKlH1sPtSKiWGijHG6Zu5OOLcTSl9Cn30d5e3GZYvlSPhK/qd7EX9yzsNNGElRYSzLSWB5Vhx3VRwn6o+/wXP2LPZp00h68kniNhVicYxvVV5rTU2bkxNV7RyvauVEVTvv1bQHu3bGR9pZaI7DW5QZx8Jp8SRH3349CfoJ+KFyv9GdsjfE2SJgTm+IezAY4lxeP2+XN1Fc2sAbZfXUd7hRCpZkJZCfl0pB3hRmpxpdKQNOJ507d9K2dRs9Bw6AUkSuXEH85s3EFBRgibg91sscjgQ6IcStabh1wtZ/H+784E0dxD9pDReA/V6jW2SjWWnrrbw1l/cFNxQkTu8/MUnKXKOr5BCLvN7OOjwdHKo7ZIS4ugNUdFQAkBKREgxwK9JWkBqZOs4tFeLatDu9HK1sDVbgyqoauCtwgoet7/Cg9SgxdOO1RuKeXkDk/A10lHlp+sWv8FZUEjZzJslPfZbY9etRtolbffb5A5xr6ApW8I5XtXG2vpOA+RF5WkJEsJvmoqx4FqTHERE2dovNj4tgiDO7U3bV94W4eYVGt0ozxDV0uCgpM2al3FvehMsbICrMyuo5KeTnTeGBuSkkXSUUe6praH+xiPZtRXirq7FERxP7yCPEb95E+J133pZVUwl0QohbS3cTVB6AbZ8HT+eVj1VWY4xHv0uLUVUatK339oDr/e5vGeL40OMGHt+7beD5eh9riON7jx3q3IPOd7V9Iee9sAvefsboKhX6+kRPMcY6BKdY7w1uA7pKSnAblsfv4UTjCfbX7udg3UFONp8koANE2CL6xsGlrWRm/Mzb8oOImLxq2pxmeDPGv52p70RrsFkU8zPiWJ6dwLKcRJZmJ5ASoeDSbgInimjb/jrN72p8PTbC0yJI+vAGYj76NCoqabyf0nXp8fg4WdPRL+TVtBlfJlotijlTYljUu3RCVjyzU2OwTvalEwJ+4//a00Vw+sW+EDd7bV8lzhGN1ppTtR2UlDZQUlbPu9XGTLwZ8RHBteFWzEjEYbv20KsDAXoOHaZ961Y6Xn8d7XQSNmMG8Zs3EfvYY9hTb58vxSTQCSEmL62NmRAr95s/B42Fpa/m/q8a/xlpf9+l1oO3BfzG9Pg6MMS+gHm/gftCbvfbFxhwvsDgbVdqS+80/WPNFg4r/7yv8pY8R4KbabgucVprzraeNRb0rjvAkfojOH1OrMrKHcl3BMfB3ZF8B3arzKQnJgd/QHO2vrNv/NulFmrbjTF+0Q4bS7ITWJadwLKcBBZlxhMZ1r/K5u/qovW3v6PlF7/A39JCxILZJN+TRFTgAKqzFiw2mH6fsdbd3PUQnTIeT3PUNHa6ebfaHItX3c6JqjbancYXY5FhVhZkxAXH4i3Kiic9Lnzif6ETCEDVgb7ulF2Xjf8jgiHuIXBE4/L62X++meLSet4oa6Cu3YVSsCgznoK8KeTnpTJ3SsyoPl9/Vxedr75K29ZtOI8eBauV6Pe9j7hNm4he8wCWsFt7AhUJdEKIycPvhcvvGt8KVu43LrsbjX0RCZC50phZLWsVvPBnRtfBgeIy4a9Pjm27R4PWNzdc/vIxGGKadFCwpW2sn+2EN9SkFXaLnXmJ86juqqbZ1QzA9LjpRjfKtFUsm7qMmLCY8WryhOBtaKDmb77EtO/9B7aUyf2B/Vbn8vo5XtXG4UstHK5o5UhFK50uo4v1lFgHy3MSWZ6TyLKcBHKnxg5bcfK1ttLyq1/R+pvnCHR2EnXvvSR/7ikil5mfPQMBqD1qdNU7/RK0XjR6EmTdbYS73EchbvJPfqG15lJzT7CKd6K6jVO1HXh8xpd1ydGOYBWvd428uMgJ8IVPMMT1VuJCQty8QpizDhzRNHS6eLOsgeLSBvaea8Lp9RMZZuXe2clmV8pUUmLGZnyh++JF2rcV0f7ii/jq67HGxxP76KNGl8x588akDWNNAp0QYuJydUD1Iag6aAS46sPgNWZEIyHHCG6ZK4zL5DlGF8Jew42h2/ADmRhlKN9bAO1Vg7dP1gB8jbx+Lz2+Hpw+p3HpdQ5/29vDb0t/S7eve9DjWLCwbvo6VqUb3SinRk0dh2czcdVt+QZtf/gD8R/6EGlf/8fxbo4I0dLtCYa3Q5daOFnTHlynbe6UGJbmJLA8J4Fl2YlMS4i4anXFW99Ay89+Ruvzz6NdLmLWriXpqaeIWDB/+DtpDfUnjbXJTr9kjNsFyFhmhLu8x4wu37cIjy9A2eXerprGxCvnG/v+rsxIjjLDnbFG3rz02OvqmnjNAgHj/93e7pSddUaIm1VgVOLmPIQOi6a0rtNY4LusgRNVxhd/6XHh5JtVuJUzkgi3j9/4Qe33071vH21bt9JVXIL2enHk5hpdMjdswJaQMG5tG20S6IQQE0dHXV/lrXK/8R+7Dhjf1k69wwhuWSuNSlxs2tUfT2a5HLl3n2dH8Zd5JjaSyzYrU31+vtjRw/qC706Y1yygA7h8rmCw6g1XwdAVcnuoIDbkfp8Tp9eJT/uu3gCTQg256HPvvnFd+Pkm0VqjXS4CPT0EnE4CPT3okOuBHicBZ4+xvd8287qzh0BrG8733jM+tNtsZP/6V0QuXjzeT+22pLWmormHQ5daOGIGuN4gEWa1cGdmHMtyElmek8CSrATiI0feXc1TVUXzT35K+9at6ECAuEfXk/TZz+KYNevaG9p0zggUpduh7rixbcodfeEuNbfv2Fvk732Hy8t7A5ZOaOg0xjbbrYp5abHBCt6dmfHMSI7Ccj3j8Qa+Xmu+BgnZZndKM8RZHX3dKec8hNsayf7zzZSUNvBGWUNwnOCdmfEU5Brj4fLSRrcr5Wjxt7XRvmMH7Vu34Tp1Cux2Yu6/n7jNm4i+994JPRHPSEigE0KMj0AAms70jX2r3A9txmx/2CNh2nIzwK0wrjuuvavapJ32exzsuLCDLXu/hkt7g9vClZ0t7/una37NvAHv4LDldV45eI1gm9PnvPrJQ4RZwoiwRxBhiyDSFmlc2o3La95m79vnsDp46IWHqOuuI75L81dFfr5XaKU9Wo37ws/a5wsJWSHhyukk0G1eOs0w1hOyrzeM9dvWP7hxDZ8BlN2OiozEEhGBxbz0NjTgb2rq9zhhOTlE37eaqNWriVy+/JYf5zJefP4Ap+s6gmPfDl1qpanLCAlxEXZz7JsR4BZkxF1XVcVdXk7Ts8/SseMVlMVC3Ps3k/Tkk4RNmzY6T6K1wgh2pduN6hHa6JmRt8GYjGPv/7lle2TUtTuDVbwTVW28W91Gt8dYOiEm3GaGO3PSlcx4UmMcVx4TfmobvP4P4HOFnEUBui/EzSuEueto8obxhjkr5Z5zTfR4/ETYrbxvdjIFeak8kJtKaszkWoLGdeYs7Vu30r59O/6WFqwpycQ99hjxmzfjmDlzvJt3XSTQCSHGhs8NtcdCKnAHwGWOzYpK7Rv7lrXSqMbd4GQRk25h3hEK6EDwx6/9fZeBAAHM7QH/oP1a6/7Hh1wGdIAvvfWl4LivULFhsXxi3ieuKXh5A94hWj40hQoGp9Ag1RueQoPUoH1X2XYzF97ufX99dEc3a49rXl+s+O0jUSN6f2mt0W53MGRp54BKl1nR0sNt6w45fkDo0h7PNT2PgaHLEhGBJSoSFRE5xLbe4yKxRPbdRw21zd7/36+3oYHzax9Eu0NmUbXZiFi6FNexY2iPBxUZSdTKlUSvXk30fauxp42gCi+G1OX2cayylUOXWjlS0cKxyjZ6zACQmRjB8uzEYICbmRJ9fRUek/PkKZp/9EM6dxajIiNJ+OAHSfzUp7BPuYkzDHZeNsPdS3DpbSOwDCUyCdb/xw1MfDXUxFWBkPsNMZZ50L5rmShrmLHQA47X2k/A78PvD6ADPnQggEX7sRDApm5gAq2IRPQXj3OmTVFS2kBxaT3Hq9rQGqbGhgfXhls1c3y7Uo4W7fXStWsXbduK6Nq1C3w+wu9cSPymzcSufwRrzOQZ8yyBTghxczhboeqdvgBXc7RvSvzkOX0BLnMFJM4Y9XXhHvzjg9R11w3aHmOP4VMLPtUXagL+QQFpxNd770tIqLrGIBUayEZyv/Fis9hurMo1TPAKt06Cmd2GoH0+Sl7+b6b87x9iC4DfAl0PryI7JssMYv0DV78KmNNpfDgbKZutL1wFQ1RouOrdFxquzIBl3scIY1H9t4WHo0LHnd5EdVu+QdsLL4A3JOzb7cQ//jhTvvJlug8epHv3brre2oW3thYAx5w5RN+3mujVq4lYtGhQSBR96jtcHL5kdJ08XNHC6doOAhosCvLSYoOTlyzLTmRq3OhUU3oOHaLpR8/SvXcvlthYEj/2MRI+/rGxH5fU3QzfnXFzz6GGWi7mZi9PM8zxg5bPMY73aQuN3V4ud3q53OGhtsNDc4+fABYCykJSdDhT46NIT4xm2elvMdRfXY3i3oitVLcalc6F0+LIzzXGw81Pj52Uf6tHytfURPv2l2nf+gLuc+Uoh4OYtWuJ37yJyJUrURbLhJ7USQKdEOLGaQ1tlf1nn+wdzG6xQ/qi/gEuKvmmNaWxp5GSyhL+5eC/jOh4q7JiUZbgT+/tYbdb+vYrpQYdf03bLVc4zxi366t7vjpkhW5K5BT+9P4/Ybfcvh+mAz09uM6cwV1Whut0Ka6yMtxnz/avNgFYrVjj4voCU2TEgMBlhKxguBq0zQxjUQPC2y3QDfFC4SbcZWWDtjtyc5lRtC14W2uN5/x5unbtpmv3bnqOHAGfD0tMDFH33GNU71bfiy355v0NmSiKjtXw3dfOUNvmJD0+gi8/NJfCxRkEAprzjV1G98kKY/23yhZjsqgIu5XFWfHB6tvirASiHaNXqdZa071nD00/ehbnkSNYk5JI/NQnSfjwh7FGR4/aea7ZcJM6RU+Bj20dcSgadh3PSRpkWro9nDCXTjhR1caJ6nZauj3sDXuaaZamQcdXB5LZMvP35OdNYU1uKlNiJ1dXytGgtcZ18iRtW7fSseMVAh0d2NLTiC8sxFNVRcfLOybkpE4S6IQQ1y7gNyYs6R37VnkAOo1v1XHEmjNPmrNPpi+BsMib2pyarhpKKkoorizmeMNxNBqrsg5Z0ZoaNZVXNr8SDDTCcKt2Ub1WvuZmM7SV4i4txVVahufSpeC4L0tcHOF5eYRlZdG2dSv4+iZTUQ4Hs4p3Trhvbiczf1cX3fv20bVrF127d+NvND6Ehi9YEOyaGb5gAco6+bt/hSo6VsNXt76H09v3N8xmUcydGk1Nm4u2HqPSmRwdxrJso/q2PCeReemx2K2j/3dNBwJ0vr6Tpmd/hPt0Kba0NJI+8xniH38/lvAJ8KFfZjUeEa011a1Ovvvv/8S37T8hUvV10e7RYXzV+yTP/Ou3xrGFE0vA7aarpIS2rdvo3rs3uF2FhTGrpHhC/a2XQCeEuDpPN9Qc6avAVR0CT6exLzajb+xb1ipIzTO+1bzJLrZfpKSyhJ0VOzndfBqA3MRc8rPyWZu9lrKWMgko1+h2mkRGBwJ4q6pwlZbhKjUD3OlSfI2NwWPs6ek45uURnptHeF4u4Xl52NLSUEpRt+UbtPzxj1hCAl3AZiPxiScm3De3twqtNe7SUrp276Zr126cJ05AIIA1IYGoe99H9Or7iH7fPVjj48e7qTekx+Pj3n97k+buweMhbRbF+5dMM7pP5iSSkxR5U7vBaa+X9h07aH72x3guXCAsO5ukp54ibsOjE69yfIvMcjkW7vn2Gyzt2MlXbM+Trpqp1Ul8x/cBjsSu5e2/XzPezZuQar7yd3S8/LLRXd7sLj6R/tZLoBNCDNbVaCwk2hvg6k5AwAcomDK/b+mArJUQnzkmTdJac7b1LMWVxRRXFFPeVg7AwuSFFGQXUJBVQGZs/7bcTgFFDC/g8eApLzeCmxng3GVlBLrN9Z6sVhwzZxKel4sjry/AWePihn68gObYukeJrLwwaJ8reyaLX3v5Zj4dYfK1ttL99j66du+ie/ce/G1tYLEQsWhRsHrnyM2d8ON+Ol1eDle0cvBCC+9cbObd6nZ8geGWxYCL3775f8MCbjftW7fS/JOf4q2pwTF3Lsmfe4qYhx665aqht6OhKsARdivf2nwHhYsn/yLuo22oSZ0mWo+MUQ90Sql1wDOAFfiJ1vrbA/Z/HvgC4Ae6gKe01qfNfV8FPmPue1pr/dqVziWBTohRoDU0n++//lvLeWOfLRwylvZV36Yth4ix+/Zba817Te8FQ1xVZxUWZWFJ6hIKsgvIz8qXhZtFP/6ODmOMW294KyvDXV4e7BqpIiKwzJ5DYOYc3Dkz6cqcSXtqJp1a0eny0eny0eX20WVedri8/W737h9OXISd33xmBblpMTel65sYmvb7cb33XrB65zp1CgBbampwWYSoVXdjjY4a55ZCe4+Xdy61cPBCM++YC3gHtLHG2MJp8dw1PZHnD1UNWaHLiI+4qRWUQHc3rb//A82/+Dn+xiYi7ryTpM9/juj775/wwVhcm+HGaIrBrjSp00Sp0o1qoFNKWYGzwFqgGjgEfLg3sJnHxGqtO8zrjwF/obVep5SaB/wOuAtIB4qBOVoPP62bBDohroPfC3XvmgHODHE95uDoiMS+td+yVkHanWBzjG3zAn6ONRwLhrj6nnpsysaKtBUUZBfwQOYDJEUkjWmbxPgKBDQ9Xj9dLh+dLi+dbh9dTi/Ousv4z5RhuXCO8EvlRFddIKqlIXi/zshYqpIyuRiXztmYdE5HTaUmKgl9lbGTUWFWosNtRDtsxITbiQletxHtsBMdbuMHJeeu+BgRdit3TItjSVYCS7LiWZKdQHL02P5bup35Ghvp2rOXrt276d67l0BXF9jtRC5dGqzehc2YMSYhpbnLzTsXWzho/pRd7kBrCLNZWJwZz4oZSayYnsiSrAQiwozq11hXUPxtbbQ89xytv/o1/vZ2IletJPlznydyxV0S5MRtb6STOo2n0Q50q4AtWuuHzNtfBdBaDznCUin1YeATWuuHBx6rlHrNfKz9w51PAp0QI+DqgOp3+tZ+qz4MvQs0J0zvP/4tefa4zOblDXg5VHeInZU7eaPyDVpcLTisDu5Ov5u12WtZPW01cY6hu7+J0TPa39hqrenx+M2qljdY3ep0GRWvTrPy1WlWwQbe7j2mx+Uho6OBGe21zGyvMS9rifN0B89VHZVMReI0apMyaZiaTWv6dFRiUjCMRYfbiDEDWnTIttiQkBbtMH6sI1iT655vv0FN2+CFzqfEOvj/1s/jaEUrxypbOVXbEew+l50UGQx4i7MSyJ0ag02qeDed9nrpOXbMWBZh127c54wwbs/IMJZFuO8+Iu+6C0tExKicr6HDxYGLZgXuYgvnGroAI5AtzU5gxfRE7pqeyJ2Z8Vdcy2ssKii+xkZafvlLWn/7OwI9PUSvWUPy554i4s47R/U8Qoiba7QD3ePAOq31k+btjwMrtNZ/OeC4LwB/A4QBa7TW55RS/wUc0Fr/xjzmp8CftNZ/HHDfp4CnALKyspZWVFSMpO1C3BpGMui7o7Z/98n6U8bCpMoKaQv7lg7IWgkx49dd0eVzsb92P8WVxbxZ9Sadnk4ibZGsnraaguwC7s24l0j7zZ0dU/QZqiIQbrPw12vnsGJGkhGy+oUwI6QZ3RL7uiSGVtG63T6GGQrUT4TdagSvcBuJFj8zuurJbq4mrbmK1MuXiL9cidVrdD8L2Oz4s2fArNnYc3OJyMsjbsE8YhJixzQcjbSC4vL6ea+mnaMVrRytbOVoZRuNncY4jMgwKwuDVbwElmQnkBg1wSaauAV5a2vp2r3HqN7t3492OlEOB5Er7jImVrlvNWGZIx8bXNPm5OCFZmMM3KUWLjYZXzREO2wsy0lgxfQk7pqeyB0ZcYTZJkaA99bU0PzTn9H2wgtor5fYhx8m6anPEj537ng3TQhxHUY70D0BPDQg0N2ltf5fwxz/EfP4Tyql/hvYPyDQvaK1fmG480mFTtxWhpuWefXfQXisEeCqDhjrwQHYoyBzeV8FLmMZOMZxnSCgx9vD7prdFFcUs7t6N06fk9iwWO7PvJ+1bD2vpQAAIABJREFU2WtZlb4Kh1W6pY0ll9fPqdp2Pv3zQ3S4hh8bNpQIuzWk8mULqXIZ3RT7VcfC7cQ4bCFdGW1E9nRhvXAW39kzwfXdPBcvBhfdtsTGEp5rzC7pyMslPG8ejhnTJ8wC09dTQemdNvxoZasZ8tooreur4uWYVbzF2UYlb+4UqeLdTAG3m55Dh42JVXbtxmN+SRw2fbrRNfP++4hcujQ4o6PWmsqWHg5eaOHARSPE9VZq4yLsLM9JZOUMowI3L21sv2QYCfeFizT/+Me0b98OShG38TGSn3ySsJyc8W6aEOIGjHeXSwvQqrWOky6XQlzFcAun9oqeYoY3cwzclDvAOnoLyl6vdnc7u6p3sbNiJ/tq9uEJeEgMTyQ/K5+CrAKWpy2/rRetHmu1bb1hoo2jla2cru3A4w9c8T4/+9SyYEjrDWNRDtuIJ/3QWuOtrg5Z382YadJXXx88xpaWRnhenhHg5uXhyM3DnpF+W4zfcXr8vFvdxtFK43dyrLKVpi6jIhkZZuXOafEsyY43gl6WVPFuJs+lS0b1btcuet55B+31QkQkbXmLOJ4+j5cc2ZT6ja6ZSVFh3DU90exCmUTu1BgsI+iuOx5cpaU0/ehZOl97DeVwEP/EEyT92aexp6WNd9OEEKNgtAOdDWNSlHygBmNSlI9orU+FHDNba33OvL4B+LrWeplSaj7wW/omRSkBZsukKOK2pzXUHoUfX2Fms6ePQ0LOuIx/G0qzs5k3qt6guKKYd+rewad9TI2aSkGWMTPl4tTFWMdgrbrbndvn51RtR193v4o2LncY6/I5bJZgd7/FWQlseelUcF+oa51VT3s8uM+f71seoNSovAW6jHFEWK04ZkzHkZtnBLi8XBy5udgSEkblOd8KtNZUtZjB2/wprevEb1bxZiRHsTgrIRjy5kyJGdG4P3F1gYDmbEMnBy+0cORMDc6DB5l76STL60tJdbYB0JM5g6jVq8l8OJ/IRYtQtvH/4mw4PUeP0fyjH9G1axeW6GgSPvIREj/5CWxJMrGUELeSawl0V/2LpbX2KaX+EngNY9mCn2mtTymlvgkc1lq/BPylUqoA8AKtwCfN+55SSj0PnAZ8wBeuFOaEuOU1lMJ7f4STL0DrxeGPi8uExOlj165hXO6+HFzo+1jDMQI6QFZMFp+Y/wkKsgpYkLzgtqi2jKfL7a6QrnytnKzpq75lxEewfHqiMeNiVgJ5abH9xvO4vP4hx4R9+aHhx9T4u7pwl5UFu0u6SkuNJQLMqZ1VRAThc+cSu+FRM7zl4Zg9G0t4+E16BW4NSimykiLJSooMduHs8fh4t7o9GMzfOtPAC0erAWOs1p2ZfWPxFmfFEx8pVbyR8Ac0pXUdHLjQzMGLLRy61EJbj/H+zYiPYMUDa8ic8TiZOYmkt9bSvWcPXW/touf3v6byuV9giY0l+n33ELV6NdH33jshgpLWmp79+2n64Y/oeecdrPHxpHzxaRI++lGssbHj3TwhxDiThcWFuNlaLsDJrUaIazgNygLT74MF7zcW9n7tq4PH0G34weCJUcZIVUcVOyt3UlJRwrtN7wIwK35WcKHvOQlzJMTdJB5fgFO17X3d9CpaqW03KmxhNgsLM+JYkt03o+KU2KuHqKJjNTy77R0+VfITfp7/JJ/bdBeFizPQWuNraMRVerpfgPNWVv7/7N13eFRl3sbx75mZ9N5ogQSClNCkgwUFBLGy1rWu7uouWMFFWRs27KIigjTbFturqOiKihQBgUV6b0IgkNDSe5nyvH9MDEERAiaZBO7PdXHlzDnPOfMbpeTO0yrvtUdHe0Nbh2QC2nvnu/knJmgT4lry81yuqsNntx6o0osXF1JlsZVI2jRSLx6A0+1hY3qedwuBlCxW7s6hoGJfwcSYYPq0iq5cxKRF9G8vyuQuKKjY1HwRhYsW4c7MBMsisFMnQs/3LqwS2LEjlq3u5tAZj4fC778nc9p0Stevx9GoEdG3/YWoP/4RW7AWmBI5ldX4xuJ1SYFOTgn5+2DT597euH2rveda9IXO10CHP0Boo8Ntq7PKZS0yxrAzd2dliNuWsw2ADjEdGJw4mEEJg2gZ0bLO6jmdHMwvPWKlxA3peZS7vL1vzSICKxbR8Aa4js1OfjW9/U88Se7HHxN81lkEdUiuHDrpzs6ubOOXkFA5XDIw2TvfzdEoTuHdx4rLXazbm1c5D2/1nlyyKzanDg1w0LVFpDfgJ0bRvUUUEcGn/tzVMpeb9Wl53lUod2WzKjWH4nJvL3TruJDKPeD6tIqhScTJ9Rwbj4fSLVu82yIsWEjJ+vVgDPboaEL79fNubH7OOdgjamfrFeNykf/Nt2RNn07ZTz/h16IFMX/9KxFXXoHNXz21IqcDBToRXyjKhM1feHvjUpcAxruJd6droOOVEFn9JbNrmzGGzdmbmZvq3eh7d/5uLCy6NermXdgkcRDNQpv5usxTSrnLw+b9h+e+rdmTW7mSnr/dRqf48Mpl7rsnRB33G1HjdOLKzsGdlYkrKwtXVhburCxcmVm4sjJxZ3rPOTMy8FQJbjgcBLRtU7FYyeH5bvZQ366WKtVjjCE1q/jwXLzUXLYeyK/cSqL1z714Fb+P2jQKrbeLelRXqdPN6j05/JiSzY+7slizJ5eyih98tG8S5g1vSd4euNra6N2Vk0PR4sUULlxE0Q8/4M7LA7udoG5dK7dFCGj7+0cveMrLyZs5k6y33sa5Zw/+Z7Qmdvhwwi++uF7P6xORmqdAJ1JXSvNg6yzvcMqd34NxQ2xbb4jrdDXEnuHrCit5jId1GeuYmzqXeXvmkV6Yjt2y07NJTwYnDGZgwkDiguN8XeYp41B+aWXP2+rUHDak51V+E9o0IrByXlT3xCg6NgsnwGHHU1aGO7MioGVm4c7+OaBleYNb5uHg5s7NPer7WgEBOGJisMfG4oiJoTw1lfLdu73bBjgcRF59FU2feqoO/0tIbSsqc7EuLbdyy4Q1e3LIqZgzFhb4cy+eN+R1bRFJRFD97sUrKnOxKjWHHyu2EFiXlovTbbBZ0KFZOH1aeXvgerWMJsoHq4Mat5uS9eu9e94tXETp5s0AOBo39m6LcP55hJx1FraQkGo/01NcTO6MGWS9/Q6ugwcJ7NiRmDuGE3bBBXU6xFNE6g8FOpHaVF4M27/1hrif5oC7DCITvAGu0zXQuGO9WZnS5XGx8uBK5qbOZf6e+WSUZOBn8+OsZmcxKGEQA1oMIDIw0tdlNnhOt4ctFb1vqyoCXHpuCRhDhHHSM9LQIwySA520spcRUlxwRC+aKysTd1b24VUjf8EWEoI9NgZHTGxFWIvBER2DIzYGe0wMjorwZo+JxRYSXNlL4Dx0iJ2DL8SUlVU+ywoI4Iy5c3DEKbyfqowx7MosqpyLuTo1h+0HC/AY719NZ8SFVs7D654QRes43/bi5ZU4WZWaXbEPXDYb0/Nwewx2m0Xn+Aj6JEXTt1UMPVpGER5Y/8Ko8+Ahihb/4O29W7IET1ER+PkR0qund2GV887Hv1XLI/5cpo+6n+bjX8UKDCTn/Q/I/te/cOfkENyzJzF33EHIOWdruLPIaU6BTqSmucph53zYOAO2fg3OIu8ecR2v8ga55j3rTYgrd5ezbP8y5qbO5fu935NblkuQI4hz489lUMIgzmt+HqH+Gl53sowxHErPYOOmXaRs38O+lH3k7z9ASHEBkWWFNHEX0dRTQmRZAQGFeVhVwlRV9oiIyl40bzCLxRET7Q1oMbE4YmMqQlrMSa8guf/Jp8j99NPKFSoB8PMj8ppraPrE4yf1TGmYCstcrNube3jI797cypUfwwMddK2Yq9k9IYquCZG1GpxyispZvju7cgjl5v35GOMdenxmiwhvD1xSNN0ToggJaFjDDE15OcVr1lK4aCGFCxdSvmMnAH4tWlT23uXPmUvejBkEJCfj3LMHT2EhIef1I3b4cIJ79PDxJxCR+kKBTqQmeNyw+wfvwiZb/guluRAU5V3UpNPVkHgO1JN914qdxSzdt5Q5qXNYlLaIQmchoX6hnN/ifAYnDObs+LMJcgT5usx6y7jduHNyKoY6ZlbORXNnZ1GekUnevoOUHMyAnGwCC/Owm19v2m0sG7aoKPzjqvSiVYa0qgEtFkd0FJZf7fc0pFxxJWVbt/7qfED79iTN/LzW31/qL2MMKZlFRwzT3HawAFPRi9emUegRK2omxZ58L15GQRnLd2VXDqHcdrAA8O6b2D0hij5J3gVMuiVEEuhXP/5OrSnlaekU/bDI23u3bBmm9Mh9IUP696fRiHsJ7NDBRxWKSH2lQCdysjweSFvh7YnbNBOKDoF/KLS/zBvikvqDo36sMFZQXsCitEXMTZ3L4vTFlLpLiQyIZGDCQAYlDKJP0z742+tHrTWt6pCl3xo66Ckvx52d7Q1mv5h/VjnM8edzOTnezd5/wWV3kOsfSnZAKLkBoZSGRhLcOI7o5k2Ib9WMxDNaENQ4DkdsLPbISM11kQatoNRZuaLmzwv35JV4e/Eigvy8cz4rQt6ZLSIIC/Rj5pp0xs3exr7cEppFBjF6SDv6JEWzfFc2yyp64FIyigAI9rfTIzGKvhWrUHZuHkGA49QKcMfiKSsjbcRIin744fCc1muvVW+5iByVAp3IiTAGDqz3zonb+Bnk7QV7ALQd4t1moM2F3r3h6oHc0ly+3/s9c1LnsGz/MpweJ3FBcVyQcAGDEwfTvXF3HLaGNUTpZKSNup+Cb74hqGdPQs7q6w1m2dmHFxTJysKTn3/Ue63gYBzR0ThiYrDFxFAQFM5+exC7XAFsKnGww+lHTkAYhUHhtExsRLfE6Mq93+IjgzSvRU4bHk9FL97PWyak5rL90OFevCZhARwqLK/cJw/AAn5+FRbooFfL6MpVKDs1C8dhP31/6KE5rSJyIhToRKojY3tFiPsUsn4CmwNaD/T2xLW7BALD66SMo/2E+4pu8YfLLM5g3p55zE2dy8qDK3EbN/Gh8QxKGMSgxEF0ieuCzTo9vkkqWbeOQ+Nfo3jZsiPO28LDcURHH3fhkPzAMNZklFUuFLE+LY8Sp3f/qriwgMo5RN0To+gcH3HKDf8S+b3yS50Vc/FymbxgR+XKrVVFBDl4/699SW4aro3Pq9CcVhE5EScS6E79H+WLVJW753CIO7ABsKDluXDW3d65ccHRdVrOzDXpPPLdv7BiviGkSS65zkge+e5icsqHYAvdyNzUuazLWIfB0CqiFbd1uo1BiYNIjk4+rXqKiletInPyFIqWLAF/f7DZKocsRVx9Fc2Osgy/y+1h28EC70p/23NYPXcHqVnFADhsFh2ahXNdrxaVw8iaR6n3TeR4wgP96Ncmjn5t4nht7vajtskvcdEpvnY23G7IStauPTLMATidlKxZ45uCROSUoR46OfUVHIRNn3tDXNpy77nmvbw9cR2ugPCmPiut12svURLxEZbt8D/yxlhYlvfPZYS9JQkBfWgV3Ie4gET87Db87BZ+dhsOuw3/3zj2s1v4220Vr3/72M9uw99uq5cbDxtjKP5xOZmTJ1O8fDn26Ggi/3gtGW+/i81ZXtnO4+9Pu3lzyQ+O8A4Lqxgati4tl+Jyb+9bbGhF71vFZsud4yMI8lfvm8jvcc4L873bc/xCfGQQSx4a6IOKREROHeqhEynO9q5MuXEG7F4MxgONO8EFT0CnqyCqpa8rJKOgjOKQ/2KzHfkTW8syeNyBBBwcRV5ZNKvdHpa7nbg8P9VaLXabhcNWEfYcNhy2irBX5djP4Q2NDtuvj/3sFn42G36OwyHx58BYNYQe+/jn51j4rVmB9d47sGEdVkwswffdT9g117Lp8WeIcLupOsDU5XIz5fZHeKXDFZWfpUPTcK7t0bwywKn3TaTmjR7Sjoc/21A5bBkgyM/O6CHtfFiViMjpR4FOTh1lBbDtG+82AzvngccF0a3hvNHe/eIatfd1hQCUOt28vXgXkxetxkrMPWobm62U1Q/fcMQ5j8fg9HhwuQ1Ot4dy9+Fjp9tDucvg8hz92On2eF+7TMV9Hpxu8+tnuI/xfLepuM9DcYm78thZpY3TbXC6PDg93uOqiyUclzH0OriFG7fNpX3OHjKCIvikyxXMTuxD+W4/eHkxk1auJtbjPuI2f4+bpIMpPDiqPd0TIunSPFK9byJ14Oe5vseaAywiIrVPgU4aNmcp/PSddzjl9tngKoHw5tD3Tuh0DTQ9s95s+O3xGGauTeel2VvIsi0kpOV3uH+jbYR/o1+ds9ksAmx2GtI+uz+H0Mqg5/bg9FQ5dhucTheeJYtwvP8ujp3bccU1JmvY38k770IG2BycWyU03sOoo76PBezq37puP5yIcEW3eAU4EREfa0DfGopUcDshZYE3xG35CsoLICQOut3s3WageW/vohn1yP92ZvHs15vZnLWZqIQvCbTvoVfTvpzT7BxeXz0Jpzm8jLWfFcDDfY8eXBqaI0JowJHXjMdDwXffkTl5CmXbt+OXkEDss88QMXTob266/dYPu446Z6dZZP3YVkJERESkrinQScPgcUPqUm+I2/wFlGRDQAR0/IN3cZOW54G9/v123nGokBe+2cLcbalEN59HaKslRATF8o9e4xjScgiWZREXHMeE1RM4UHSAJiFNGNl9JJcmXerr0muNcbvJ//obMqdOpXznTvxbtaLZSy8SfsklWI5j/z/UnB0RERGRI2mVS6m/jIH01d6FTTZ9DgX7wS/Yu0dcp6vhjAvAEXD85/hAVmEZE+b9xPs/phIUtZ7gJl9TZvK5of0N3N31bsL8w3xdYp0zLhd5//2KrGnTKN+9m4A2ZxBzxx2EX3QRlr36c96Ot2+fiIiISEOnVS6l4TIGDm0+vFdczm6w+8MZg6Hz1dD2IvAP8XWVv6nU6ebdJbuZ/P0OSqz9JHT4lkz3Js6I7syYvmPoENPB1yXWOVNeTu4XX5A1bTrOtDQC2rcnfsIEwgYPwjqJobGasyMiIiJymAKd1A9ZO2HjZ97euIytYNkh6Xw47x/Q/lIIivR1hcfk8Rj+u34fL327jfS8PNq1/5FD1mzK7EE81usxrm5zNXbb6bXyoqe8nLxPPyXzzTdx7dtPYKdONH7kEUIH9NcWAiIiIiI1RIFOfCcvzTuUcsMM2L/Wey7hbLjkZe+G36Fxvq2vmpbvyubZWZtZl5ZHUkIqLVt+zr6yAwxNGsqoHqOICYrxdYl1ylNaSu7Hn5D11lu4Dh0iqGtXmj71FCHnnqsgJyIiIlLDFOikbhVmwOaZ3t64PUu955p1gwufgY5XQkRz39Z3AnZlFvHCN1uYvekgjaJK6NVnDlvzl5IUmMS4/u/Qq0kvX5dYpzzFxeR89H9kvfMO7sxMgnv2pNmLLxDct6+CnIiIiEgtUaCTmrf+Y5g31tsDF9Ec+j3gXYFy46eQshCMG+Law4Ax0OkqiGlY+4flFJUzYd5PvLcslQCHYXDfzawr+oTdRYaR3Udya4db8bMffdn9U5G7sIicDz4g+913cefkEHxWX2JffYWQ3r19XZqIiIjIKU+BTmrW+o/hvyPAWbFXWN5e+Gqk9zgyEc69z7tCZaMO9WbD7+oqc7n519LdTJy/g6IyF4O7lbDf7z2W5e2kf/P+PNTnIeJDT5/FOtz5+WS/9x7Z//o3nrw8Qvr1I/bOOwnu3s3XpYmIiIicNhTopGbNG3s4zFUV2ghGrmtwIQ7AGMOsDft58dut7M0u4dx2gcS0+J756bNoamvK6wNeZ0DCAF+XWWfcublk//vfZP/7P3gKCwkdMIDYu+4kqHNnX5cmIiIictpRoJOalZd29POFGQ0yzK1KzeaZWVtYsyeXdk1CuOOyDL5Ke4tN+4q4rdNtDO8ynGC/YF+XWSdc2dlkv/tPct5/H09xMWGDBxN75x0Edjj9tmIQERERqS8U6KRmhTaGwgO/Pt+AFjsBSM0q4sVvt/L1hgM0Cgtg1GWh/Jj/Ju/vXEePxj0Y02cMZ0Sd4esy64QrI4Osd94l56OPMKWlhF98MTF3DCewbVtflyYiIiJy2lOgk5oV1uTXgc4vCC543Df1nKDc4nImzt/Bv/+3G4fNxl0Dm+MK/4Z3tn9EuH84z5zzDENbDz0tVm10HjxI1ltvk/vxxxink/DLLiX2jjsISErydWkiIiIiUkGBTmpO2irvfnLJV8C+VYdXubzgcejyR19Xd0zlLg//WZbK6/N+Ir/UybXdm9OrUxpTN44iY38G17S9hpHdRxIREOHrUmudMz2dzLfeIm/GpxhjiBg6lNjhw/BPTPR1aSIiIiLyCwp0UnPmj4XgGLhiEgSE+bqaajHG8O3GA7zw7VZSs4rp1yaW2/qH8X+7Xmfs8iW0j27Pq/1f5cy4M31daq0r37uXrOnTyf18JlgWkVdeScywv+HfvGENlxURERE5nSjQSc3YtQhSFsCQ5xpMmFuzJ4dnZ21hZWoObRuH8uatZ7Kz/CtG/+8tHDYHD/Z6kOvbX4/Ddmr/MSnbtYusadPJ++9/sex2oq67jpi/3o5f06a+Lk1EREREjuPU/k5V6oYxMO9pCI+Hnrf7uprj2ptdzIvfbuWr9fuJDQ3g+as606JZGi+suIPU/FSGtBzC6J6jaRzS2Nel1qqyHTvInDKV/G++wfL3J/rmm4m+7Tb8GjfydWkiIiIiUk0KdPL7/fQdpC2Hy14Dv0BfV/Ob8kqcTP5+B+8u2Y3NBiMGnsFVvcOZvG48z837hoSwBKYNmsbZ8Wf7utRaVbptG5lTplIwezZWUBAxt/2F6D//GUdsrK9LExEREZETpEAnv4/H4+2di2oF3W72dTVH5XR7eH9ZKhPm/URuiZOruzfn74PPYMH+mdzw9STK3eXcdeZd3Nb5NgLsAb4ut9aUbNpE5pQpFM6dhy0khJjhw4i+9VYcUVG+Lk1ERERETlK1Ap1lWRcBEwA78JYx5oVfXB8F/BVwARnAbcaY1IprbmBDRdM9xpihNVS71AebP4eDG+CqN8Hu5+tqjmCM4bvNB3nhm63syizi7NYxPHppMsZ/L3//4S9syd7C2c3O5pE+j5AYfuqu4Fiybh2Zk6dQuHAhtvBwYu+5h+g/3Yw94tRfsVNERETkVHfcQGdZlh14AxgMpAErLMv60hizuUqzNUBPY0yxZVl3Ai8B11VcKzHGdK3huqU+cLvg++egUQfodLWvqznC+rRcnpm1heW7sjmjUSjv/LknPVoGMnHtRD7e9jGxQbGMO38cQxKHnLJ7yhWvWkXmG5MpWroUe0QEcffdR9RNN2IPaxiL1oiIiIjI8VWnh643sMMYkwJgWdZHwB+AykBnjPm+SvtlQP0ceyc1a92HkLUDrv8AbHZfVwNAem4J477dysy1+4gJ8eeZKzpxXc/mfJv6NUO/eJncslxuSr6Ju7veTah/qK/LrXHGGIp/XE7m5MkUL1+OPSaGRqMfIOr667GFhPi6PBERERGpYdUJdPHA3iqv04A+x2h/O/BNldeBlmWtxDsc8wVjzMxf3mBZ1jBgGEBCQkI1ShKfc5XBwhchvge0u8TX1VBQ6mTygp28vXgXFnD3gNbccX5rMkr3Mnze31hxYAVdYrswddBUkmOSfV1ujTPGULRkKZmTJ1OyejWOuDgaP/wQkX/8I7agIF+XJyIiIiK1pDqB7mjj0cxRG1rWzUBP4PwqpxOMMfssy0oC5luWtcEYs/OIhxkzHZgO0LNnz6M+W+qZVf+EvL0wdCL4cMiiy+3hw+V7eG3uT2QVlXNVt3juH9KO6FCYvn4y/9z0T4IdwTx+1uNc3eZqbJbNZ7XWBmMMhQsXkjl5CqXr1+No0oTGj40h8pprsAWcugu8iIiIiIhXdQJdGtCiyuvmwL5fNrIsaxDwKHC+Mabs5/PGmH0VX1Msy1oAdAN2/vJ+aUDKi2DROGjZD5L6+6QEYwzztx7iua+3sDOjiD6tovnnpR3o3DyChXsX8tzc59hXtI+hrYcyqscoYoJifFJnbTEeD4Xz53uD3ObN+MXH0+Spp4i48gps/v6+Lk9ERERE6kh1At0KoI1lWa2AdOB64MaqDSzL6gZMAy4yxhyqcj4KKDbGlFmWFQucg3fBFGnIfpwGRRlw3fs+6Z3bmJ7Hs7O28L+ULJJiQ3jzlp4MSm7EgaIDjJz/OPP3zqd1RGveHfIuPZv0rPP6apPxeCj47jsyJ0+hbPt2/BITaPrss0QMvRzLr36tMioiIiIite+4gc4Y47Is6x5gNt5tC94xxmyyLGsssNIY8yUwDggFPqlYMfDn7QmSgWmWZXkAG945dJuP+kbSMJTkwpLXoO1FkHCsqZQ1b39eCS/P3s5na9KICvZn7B86ckPvBLDcvLvpXaaumwrAfd3v45YOt+BXz7ZR+D2M203+19+QOXUq5Tt34p+URLOXXiT8kkuwHNpOUkREROR0Va3vBI0xXwNf/+Lc41WOB/3GfUuBzr+nQKlnlk6E0jwY8GidvWVhmYupC3by1uIUPAaGnZfE3QPOIDzQj1UHV/HMsmfYkbuDAS0G8FDvh2gW2qzOaqttxukk76tZZE2dSnlqKgFtziD+1VcIGzIEy14/VhYVEREREd/Rj/al+gozYNkU6HgVNO1S62/ncnv4eGUar87ZTmZhGUPPbMboIe1oER1Mdmk2jy5+hS93fkmzkGZMHDiR/i3613pNdcWUl5P7xRdkTZuOMy2NgORk4l+fQNigQVi2U2thFxERERE5eQp0Un2LXwVXCQx4pFbfxhjDgu0ZPP/1FrYfLKRXyyjeurUnXVtE4jEePtn+Ca+teo1iZzG3d7qdYV2GEewXXKs11TbnoUOkj7qfZi+9SNHChWS++SauffsJ7NyZxo88QuiA/qfsBugiIiIicvIU6KR68tJgxdvQ9UaIbVNrb7N5Xz7Pfb2FxTsyaRkTzNSbuzOkYxMsy2Jr9laeXvY06zOQyshVAAAgAElEQVTW07NxT8b0HUPryNa1Vktdypg4iZJVq0i55FJMaSlBXbvS9KmnCDn3XAU5EREREflNCnRSPQtfAgyc/2CtPP5gfimvfLeNT1alERHkxxOXd+CmPon4O2wUOYuYtGYSH2z9gMiASJ4991kuT7q8wQYd43JRvns3pVu2UrplCyXr11GycpX3WlkZ8a+N986Ra6CfT0RERETqjgKdHF/WTljzHvT6K0Qm1Oiji8pcTF+UwvRFKbg9hr+e24p7BrQhItgPYwyzd8/mpeUvkVGSwbVtr2VE9xFEBETUaA21yVNSQtm2bZRu3Urp5i2Ubt1K2fbtmNJSACx/f2yhoWCzgccDDgdFPy4n/KKLfFy5iIiIiDQECnRyfAueB0cA9Lu/xh7p9hhmrNrLK99t51BBGZd2acqDQ9qTEOOdC7cnfw/P/fgcS/YtITk6mfEDxtMlrvYXYvk9XNnZlG7ZQtmWLZW9b+W7d3uDGmCLiCCwfXuirr+ewOT2BCQnYw8NZefFl1S2wekk77PPiLvrThxxcb77MCIiIiLSICjQybEd3AQbZsC590FY4xp55KLtGTz39Ra2Hiige0IkU27uQY/EKADK3GW8s+Ed3trwFn52Px7q/RDXtbsOh63+/FY1xuBMS6vocdtCWUXPm+vgwco2jmZNCUzuQPjFFxPYIZnA9u1xNGv2q2GU+598CvNzmPv5+R4PGZOn0PSJxxEREREROZb6812y1E/zn4WAcDh7xO9+1LYDBTz39RYWbs+gRXQQb9zYnUs6N6kMOUv3LeW5H58jNT+Vi1pexOheo2kU3Oh3v+/vYcrLKdu5s7LHrWyLN7x5Cgu9Dex2ApKSCO7Tm8DkDgQmtyewfXvskZHVen7J2rXgdB550umkZM2aGv4kIiIiInIqUqCT35a2ErbNgoFjIDi62rfNXJPOuNnb2JdbQrPIIIaf34ot+wv4vxV7CQ1wMObSZP50ViIBDu/G2IeKDzFuxTi+3f0tieGJTBs8jbObnV1bn+o3uQsLDw+X3FoR4HbsqAxcVlAQge3aETH0cgLatycwuQMBbc7AFhh40u+ZNPPzmipfRERERE5DCnTy2+aNheBY6HNntW+ZuSadhz/bQInTDUB6bgmPf7EZmwV/PrsVIy44g8hgfwBcHhcfbf2ISWsn4XQ7uavrXdzW6TYC7AG18nF+ZozBdejQr+a7OffurWxjj4khMDmZ0HPPITA5mYD2yfgnJmDZ7bVam4iIiIjIiVCgk6NLWQi7FsKQ5yEgtNq3jZu9rTLMVRUXFsDjl3eofL0+Yz3PLHuGLdlbOKfZOTzS5xESwmt2BU0A43ZTnppK6eYtlG3dUrnSpDs7u7KNX2ICgR06EHn11ZWLlTji4rRtgIiIiIjUewp08mvGwPynITweet52Qrfuyy056vlD+WUA5JXlMWH1BGZsn0FcUBwvn/8yFyZeWCPhyVNaStn27RU9bpsp27KV0u3bMSXemiw/PwLatCF0QP/K+W4B7dpjDw353e8tIiIiIuILCnTya9tnQ9oKuHwC+J3Y/LDYsAAyCsp+db5pZCBf7vySV1a+Qm5ZLjcl38TdXe8m1L/6vX9VuXJyKKuyt1vpls2Up+w6vEVAWJh3i4A/XktA+2QCOyQT0KoVlr//Sb2fiIiIiEh9pEAnR/J4vL1z0UnQ9aYTujW3uByny4MFmCrng4IziTljNo8u3kCXuC5MGzyN9tHtq/VMYwzO9PSK+W7euW6lW7fi2r+/so2jSRMCk5MJv/BCApKTCUxOxi8+XkMmRUREROSUp0AnR9r0GRzcCFe9BXa/at/m8RhGfbyOonIXl529n4UZ/8Zjz8EygWArI9sZxhNnPcFVba7CZtmO+gzjdFKWkvKr+W6eggJvA5sN/6RWBPfoQWBy8uH5blFRNfHJRUREREQaHAU6Ocztgu+fg0YdodPVJ3TrlIU7mb/1EDcMyGBuxjQiSku4b6ab8VeUUBjmYET3EVzT9prDb1VYRNm2rUfMdyv76SfMz1sEBAYS0K4t4ZdeQuDPQybbtMEWFFSjH1lEREREpCFToJPD1n0A2Tvh+g/BdvRetKNZuiOTV77bxtAzm7GqYAKl7lJuWuyhfRpcvcTDZ+e4WPrpJC6Izq2c7+ZM3VN5vz0qisDkZKJu+VPlYiX+LVtqiwARERERkeNQoBMvVxkseBHie0C7i6t928H8UkZ8tIakuFCev6ozfT/aT2yuhwvWGWwGhqw2XLTaDWSSwWv4tWhBYHIykVdcUTnfzdGokea7iYiIiIicBAU68Vr5LuSnwRVvQDXDldPt4Z4PVlNc7uajYd1ZlbEUgIc+9uDwLjaJB9geD3MuimPSHbOwh4XV0gcQERERETn9KNAJlBfBDy9Dy36Q1L/at42bvY0Vu3N4/YZu5JufuH/B/Vy/NpSErNzKNnag9UEIHnCnwpyIiIiISA2r/kQpOXX9OBWKMuCCx6t9y7cbDzB9UQq3nJVIuxYF3DPvHnrkRnLV7PwjtiwAcGDnzFk/1WzNIiIiIiKiHrrTXkkuLJkAbS+GFr2rdcvuzCJGf7KOM1tEcst5Ifz1u78QVx7AfTPKMTYbVsXm3j+zudyUrFlTG9WLiIiIiJzWFOhOd0snQmkeDHy0Ws1LnW7ufH81drvF2Kuac8/84XjcLl6c1wKyN9Pyww8J6typlosWERERERHQkMvTW+EhWDbFu+dck87VuuXxLzay9UA+z1ydxJPL7yOnNIcpKf0wK9bS5InHFeZEREREROqQAt3p7IdXwVUK/R+pVvOPV+zl45Vp3HF+cz5MfYLU/FTecPwJ23szibzuOiKvPrHNyEVERERE5PdRoDtd5e6FlW9D1xsh9ozjNt+0L4/HvtjIWWeEs9M2mY2ZGxnf8n7CXnyXwDO70PjR6oVCERERERGpOQp0p6tFL3m/nv/gcZvmlzq56/3VRAY7iGn5Gf/bv5SxZz5E8+c/wAoMpPmECdj8/Wu5YBERERER+SUFutNR1k5Y8z70vA0iWxyzqTGGBz5eR3pOMb16fs/C9Lnc330UPd76H+WpqcSPH49fkyZ1VLiIiIiIiFSlQHc6+v45cARAv/uP2/StH3bx3eaDnNdnJQv3/5fbO93OZctcFMyZQ6MHHiCkT/W2OhARERERkZqnQHe6ObARNs6APndAaKNjNl2xO5sXvt1Klw5rWZE7g2vaXsNfS3uRMf41wi+5mOg/31pHRYuIiIiIyNFoH7rTzffPQkAEnDPimM0yCsq4+/3VNGq6nl3mIy5MvJAHm9/Gnmv/SEDrJJo+/TSWZdVR0SIiIiIicjQKdKeTvStg29cw8DEIivrNZm6PYcSHayiwr8Ev/EPOanoWz/Z+kv1/+gvG5aL5xInYQkLqsHARERERETkaBbrTyfynISTOO9zyGF6ds43lB5YT1vIDOsZ0Ynz/8WQ/+RylmzbRfPJk/Fu2rJt6RURERETkmDSH7nSRsgB2LfQuhBIQ+pvN5m89yJT/LSQs8T+0DE/kjQveoPyzr8j77DNi77qTsIED6q5mERERERE5pmoFOsuyLrIsa5tlWTssy3roKNdHWZa12bKs9ZZlzbMsK7HKtVsty/qp4pdW0fAFY2De0xAeDz3+8pvN9mYXc99n3xGW+E8ah0QzbfA0Arbu5sCzzxJyXj9i7767DosWEREREZHjOW6gsyzLDrwBXAx0AG6wLKvDL5qtAXoaY7oAM4CXKu6NBp4A+gC9gScsy/rtyVtSO7Z/C+krvZuI+wUetUmZy83wD+diGk8jPNCP6RdOJ6bETtrI+/Br3Jj4l17CstvruHARERERETmW6vTQ9QZ2GGNSjDHlwEfAH6o2MMZ8b4wprni5DGhecTwEmGOMyTbG5ABzgItqpnSpFo/H2zsX3Rq63vibzcZ8uYzdfq8RGODkzSHTSQiOJ/3vo3Dn5dF80kTskZF1WLSIiIiIiFRHdQJdPLC3yuu0inO/5XbgmxO517KsYZZlrbQsa2VGRkY1SpJq2/QZHNoEAx4Bu99Rm3y0cjuzDj2Nf0AuUwdPpn10ew69/ArFK1bQdOxTBLZvX8dFi4iIiIhIdVQn0B1tszFz1IaWdTPQExh3IvcaY6YbY3oaY3rGxcVVoySpFrfTu+9co47Q8aqjNtm4L5OnV/wDe+B+xvd/lR6Ne5A3axbZ//wnUTffTMTQoXVctIiIiIiIVFd1Al0a0KLK6+bAvl82sixrEPAoMNQYU3Yi90otWfsBZKfAwDFg+/X/6tySUm79agS24J083OtJBiT2p3T7dvaPeYygHj1o/I/RPihaRERERESqqzqBbgXQxrKsVpZl+QPXA19WbWBZVjdgGt4wd6jKpdnAhZZlRVUshnJhxTmpbc5SWPgSxPeEdhf/6rLb4+aaT/5OecAGrk8awY0dr8Sdn0/avfdiCw0hfvyrWP7+PihcRERERESq67gbixtjXJZl3YM3iNmBd4wxmyzLGgusNMZ8iXeIZSjwiWVZAHuMMUONMdmWZT2NNxQCjDXGZNfKJ5EjrXoX8tPgislgHTny1RjD7f99koNmMb0irufRfn/DeDzs+8eDONP3kfjvf+HXqJGPChcRERERkeo6bqADMMZ8DXz9i3OPVzkedIx73wHeOdkC5SSUFcKil6HVeZB0/q8uj/1hIqtyZ9KYC3jz8ocByJw6lcIFC2g8ZgzB3bvXdcUiIiIiInISqrWxuDQwP06F4kwY+PivLr27/gNm7HoTv5IefHLNi9jtNgoXLSJz4iQi/jCUqJt+e2sDERERERGpX6rVQycNSEkOLHkd2l4MLXodcenrlG94dfULuIvb85/LXyEqJIDyPXtIf2A0Ae3a0eTJJ7Gsoy1MKiIiIiIi9ZF66E41SydCWZ53ZcsqlqQv4eEfHsFVksiD3Z6la4sYPCUlpN07AiyL5hNfxxYU5KOiRURERETkZKiH7lRSeAiWTYFO10CTTpWn1x5ay4j59+EsjWNQ9EPc0rcNxhj2P/4EZdu302L6NPxbtDjGg0VEREREpD5SD92p5IdXwFUGAx6pPLU9Zzt3zr2L8rJQmpWM4MUr+2BZFjnvvU/+f/9L3Ih7Ce3Xz4dFi4iIiIjIyVKgO1Xk7oWV70C3myCmNQB7C/YyfM4dlJTZMPuHMe2m/gT7OyhetYqDL75I6MCBxAwf7uPCRURERETkZGnI5ali4Yver+c/CEBmSSbD5wwnv7SE/F1/Y+I1/WkdF4rz4CHS7rsP//h4mr34ApZNmV5EREREpKFSoDsVZO6AtR9A72EQ0Zz88nyGzxnOwaIMclJu49Zefbi0S1NMeTnp992Hp6iYhLffxh4W5uvKRURERETkd1CgOxUseA4cgdBvFCWuEu6Zdw8puSmUp/+Fro268PDFyQAcfPElStasIX78qwS2bevjokVERERE5PfSeLuG7sAG2Pgp9L0DZ1AUoxaMYl3GOsLybyXAlcykG7vj77CRO3MmOe+/T/Rf/kL4xRf7umoREREREakBCnQN3fxnITACz1l38+iSR1mcvph29j+zN70NE67vSrPIIEo3b+bAE08S3Ls3je4f5euKRURERESkhijQNWR7V8D2bzBnj+C59VP4Ztc39I/7M8s3tOW+C9rSr00c7txc0u4dgT0qivjxr2I5NMpWRERERORUoe/uG7L5YyEkjjeC7fzfpv/jsoQb+XRuB85rG8O9A8/AuN2kPzAa16FDJL73HxwxMb6uWEREREREapB66BqqlAWwaxHvdRrMtE3vcGnLP7BoeW9iQ/157bqu2GwWGZMmUbR4MY3HjCHozDN9XbGIiIiIiNQwBbqGyBiYN5YvY5vz4qHFDGxxAYd2Xcah/DLeuKk70SH+FMybR9aUqURcczWRf7zW1xWLiIiIiEgtUKBriLZ9w4LszTweZqdPkz6cYQ1n/tYsxlzagW4JUZTt2sW+Bx8isFMnmjz2GJZl+bpiERERERGpBQp0DY3Hw8oFT/JA4ziSYzpwU8snGP9dCpd1acotZyXiKSoi7d57sfz8aP76BGwBAb6uWEREREREaokWRWlgtqx4g3sDiogPasTYPq9xw7SNtIoN4YWruwCw79ExlKfsIuHtt/Br1szH1YqIiIiISG1SD10DsjtnB3dsnk4YdiZf/B6PfraLojIXU27uQWiAg+x3/0nBt9/SaNTfCTnrLF+XKyIiIiIitUyBroE4UHSAYd/cAsbN9G4P8O8l+Szflc3zV3WmbeMwipb9yKGXXybswguJvv12X5crIiIiIiJ1QIGuAcgpzWH4d8PIL8tniieO7Y7BTFuYws19E7iiWzzO/ftJHzUK/1ataPrcc1oERURERETkNKE5dPVckbOIu+beRXrBHqYcPET0xa9y3Yz1dGkewWOXdcBTVkbaiJGYsjKaT3wde2iIr0sWEREREZE6okBXj5W7yxn5/Ui2ZG/htexiujfpy+ULgrBZJbxxY3cCHHb2P/UUpRs2ED/xdQKSknxdsoiIiIiI1CENuaynXB4XDy56kB/3/8jTMX3pn3uQqfYb2Lw/n/HXnUmL6GByPvmE3E8+IWbYMMIHD/Z1ySIiIiIiUscU6OohYwxPL3uauXvm8mDXe7l8/dekNx7AuE3h3D2gNQPbN6ZkwwYOjn2akLPPJm7kCF+XLCIiIiIiPqBAVw+NXz2ez376jGFdhnFzxn5MWT53pl/E2a1jGDW4Ha7sbNJGjMQRF0ezV17Gstt9XbKIiIiIiPiA5tDVM+9sfId3N77Lde2u457W12K+7sZcez8O+J3BrOu7YfO42TPqftzZ2SR+8D6OqChflywiIiIiIj6iHrp65NPtnzJ+1Xgubnkxj/R5BBa/gsdZyvMlVzLpxu7EhQWQ8dprFC9bRpMnniCoY0dflywiIiIiIj6kQFdPzEmdw9hlYzkn/hyePfdZbHlpeFa8y/+5zueGi/rTu1U0+d9+S9ZbbxN5w/VEXnWlr0sWEREREREfU6CrB/637388uOhBusR2YXz/8fjZ/ciY9TRuj2F962H8tV8rynbsYN8jjxJ05pk0efhhX5csIiIiIiL1gAKdj63PWM/I70fSMqIlky6YRJAjiJw9m4j6aQYz/S7mkRsG4yksJO2ee7EFBRH/+gQsf39fly0iIiIiIvWAAp0P7cjZwV3z7iImMIZpg6YRERCB22PY+uHDlBk/zrxhLGH+dvY9/DDle/fS/LXx+DVu7OuyRURERESknlCg85H0wnSGzxmOv82f6RdOJy44DoAPvviKs0oWktrmVtq1TiLrzbconDuPxv8YTXCvXj6uWkRERERE6pNqBTrLsi6yLGubZVk7LMt66CjXz7Msa7VlWS7Lsq75xTW3ZVlrK359WVOFN2SZJZkM+24YJe4Spg6eSouwFgB8v+0QTVe/QrEtlA5XP0rh4iVkvPYa4ZdeStQtt/i4ahERERERqW+Ouw+dZVl24A1gMJAGrLAs60tjzOYqzfYAfwYeOMojSowxXWug1lNCQXkBd869k4ySDKYPnk7bqLYApOUU8+5H/8e/7WtwnvcY5VlF7Lv/fgLatKHp02OxLMvHlYuIiIiISH1TnY3FewM7jDEpAJZlfQT8AagMdMaY3RXXPLVQ4ymj1FXKPfPuYUfuDiYNnETXRt6cW+Zyc/d7q3jU8yHu4Fjs3f/C7j//DePx0Hzi69iCg31cuYiIiIiI1EfVGXIZD+yt8jqt4lx1BVqWtdKyrGWWZV1xtAaWZQ2raLMyIyPjBB7dcDg9Th5Y+ABrDq3h+XOf55z4cyqvPTtrC6H7l9Db2oTt/NEceO5lyjZvodm4l/BPTPRh1SIiIiIiUp9Vp4fuaGP9zAm8R4IxZp9lWUnAfMuyNhhjdh7xMGOmA9MBevbseSLPbhA8xsPjSx5nYdpCHuv7GBe1uqjy2hdr0/n3/3azNOYLcLQgd0cIeTNnEnv33YT17++zmkVEREREpP6rTg9dGtCiyuvmwL7qvoExZl/F1xRgAdDtBOpr8IwxvLTiJb5K+Yp7u93LH9v9sfLaTwcLePizDdzZZBvNijZT3PQmDrw4jpDzzyP27rt8WLWIiIiIiDQE1Ql0K4A2lmW1sizLH7geqNZqlZZlRVmWFVBxHAucQ5W5d6eDqeun8v6W9/lThz/xt85/qzxfVObizvdXE+oHo+wf4wpsTfrkb/Fr0oT4l17CsmlHCRERERERObbjpgZjjAu4B5gNbAE+NsZssixrrGVZQwEsy+plWVYacC0wzbKsTRW3JwMrLctaB3wPvPCL1TFPaR9s+YDJaycztPVQHuj5QOVKlcYYHv5sAykZhbx31j4cGVtJ+7ER7vx8mk+aiD0iwseVi4iIiIhIQ1CdOXQYY74Gvv7FucerHK/AOxTzl/ctBTr/zhobpFkps3h++fMMaDGAp85+Cpt1ODu/tyyVL9ft48HBSbTd+CgHtidRsiWVZuPGEdiunQ+rFhERERGRhkTj+mrBorRFjFk8hl5NejHu/HE4bIdz89q9uYz9ajMD2zdiePgy8tYcIGdtKVG3/ImIyy/zYdUiIiIiItLQKNDVsNUHVzNqwSjaRrfl9QGvE2APqLyWU1TO3e+vplFYIK9e1Y7yz19i/8pognr0oPHo0T6sWkREREREGiIFuhq0LXsb98y7h6YhTZkyaAqh/qGV1zwew98/XktGQRlTbu5O2PI3SfvOhT0iguavjcfy8/Nh5SIiIiIi0hAp0NWQPfl7GD5nOMF+wUwfPJ3owOgjrr/x/Q4WbMvg8cs70DnGIv2F6TiLHcRPmowjLs5HVYuIiIiISENWrUVR5NgOFR9i2JxheIyH6RdOp2lo0yOuL/4pk1fnbueKrs24qU8CmaNvoijNRuN7byW422m1LZ+IiIiIiNQgBbrfKa8sj+FzhpNTmsM7Q94hKSLpiOsH8koZ+dEa2jQK5bmrOlM4+ysyv1pDxJnRRN31oI+qFhERERGRU4EC3e9Q7Czmrnl3sSd/D1MGTaFjbMcjrjvdHu7+YDWlTjeTb+qBY386ex9+lIAoJ03GTazcl05ERERERORkKNCdpHJ3OX9f8Hc2Zm7k1f6v0rtp71+1efGbraxKzWHiDd1ICrWx+7a7sNxlNL/9LGwJ3X1QtYiIiIiInEoU6E6C2+Pm4R8eZum+pYw9eywXJFzwqzbfbNjPW4t38eezW3JZl6bse2A0ZTtTaHF+Hv5XPln3RYuIiIiIyClHq1yeIGMMz/z4DN+lfscDPR/gyjZX/qrNrswiRs9YT9cWkTxySTI5//kP+bNmEdeliNBLr4OY1j6oXERERERETjUKdCfo9TWvM2P7DP7a+a/c2vHWX10vKXdz53ur8LNbvHFTd1xrVnHwxZcI7RBLTMdSOO8fPqhaRERERERORRpyeRyzUmYxYfUEDhQdIMw/jPzyfK5tey0juo34VVtjDGNmbmTbwQL++ZfeNCrLZ9ffR+HfrAnNOqzG6n0HRMT74FOIiIiIiMipSIHuGGalzOLJpU9S6i4FIL88H5tlo1tct6OuUPnxyr18ujqNkRe0oV/LCPb86RY8xcUk3tAUe14Q9BtV1x9BREREREROYRpyeQwTVk+oDHM/8xgPE9dO/FXbjel5PPbFJvq1iWXEBW04+PzzlKxbR7N/DCcgYzb0vRNCYuuqdBEREREROQ0o0B3DgaID1TqfV+LkrvdXExPiz2vXdaVg5kxyP/yI6NtvI9w9FwIj4ax76qJkERERERE5jSjQHUOTkCbHPW+M4YFP1rEvt4RJN3YnOHUHB558kuC+fWl0zTnw02w4ZyQERdZV2SIiIiIicppQoDuGkd1HEmgPPOJcoD2Qkd1HVr6eviiFOZsP8sglyZwZDun3jsAeE0P8Ky9jLXgWQhpBn+F1XbqIiIiIiJwGtCjKMVyadClA5SqXTUKaMLL7yMrzP6Zk8dLsbVzauSl/7tuCtGHDcWVkkPjB+zhy10HqYrh4HPiH+PJjiIiIiIjIKUqB7jguTbq0MsBVdaiglHs+XENidDAvXN2ZzImTKFq6lCZPjyWoUyd4cyBEtIAev96rTkREREREpCZoyOVJcLk9jPhwDQWlTibf3B3zwwKypk0j8tpribr2Wtg6C/athv4PgSPA1+WKiIiIiMgpSoHuJLwyZzvLUrJ57srOtCrOZP9DDxPYpQuNHxsDHjfMfwZi2kCX631dqoiIiIiInMI05PIEzd18kCkLdnJD7wT+0DaK3dddhxUQQPMJr2Hz94f1H0PGFrjmXbDrP6+IiIiIiNQeJY4TsCermFEfr6VTfDiPX5bM/gfup3zXLhLeeQe/pk3B7YTvn4UmnaHDFb4uV0RERERETnEKdMcxc00642ZvY19uCQ67hd2CKTf1oPg//6Lgu+9oNHo0IX37eBuv+Q/k7IYbPwabRrOKiIiIiEjtUqA7hplr0nn4sw2UON0AON0Gy25jy6x5tHjlVcIuuojo2/7ibewsgYXjoEUfaHOhD6sWEREREZHThQLdMYybva0yzP0soiCLyJcn4J/UimbPPoNlWd4LK96Ggn1w1XT4+ZyIiIiIiEgt0rjAY9iXW3LEaz+3kzHL/4XN7aL5xInYQio2DC8rgMWvQtIAaNXPB5WKiIiIiMjpSD10x9AsMoj03BKiSvN5aMV7ZAaF0zY3jUkDh/FGq1aHGy6bAsVZcMFjvitWREREREROO+qhO4bRQ9oR5Gfnxq1z6JyVwsC0tXySPJjBf/vj4UbF2bB0IrS/DOJ7+K5YERERERE57aiH7hiu6BaPPSeLxE9XYAEeLLrdcxuXd4s/3GjJBO+QywGP+qxOERERERE5PSnQHUfPRZ+TawM8YPNz0HPxl3BBV+/FggPw4zTofC007uDTOkWqcjqdpKWlUVpa6utSRETkNBMYGEjz5s3x8/PzdSkipwUFumNwHjpE3uefg8tVccJJ3mefEVHaXbsAABOVSURBVHfXnTji4uCHV8DjhP4P+bZQkV9IS0sjLCyMli1bHl6JVUREpJYZY8jKyiItLY1WVdcbEJFaozl0x5A5eQrG4zninPF4yJg8BXJSYeW70O1PENPaRxWKHF1paSkxMTEKcyIiUqcsyyImJkYjRETqkHrojqFk7VpwOo886XRSsmYNLDwAlg3OG+2b4kSOQ2FORER8Qf/+iNStagU6y7IuAiYAduAtY8wLv7h+HvAa0AW43hgzo8q1W4ExFS+fMcb8qyYKrwtJMz8/+oWM7TC5D/S9CyLij95GRERERESklh13yKVlWXbgDeBioANwg2VZv1wBZA/wZ+CDX9wbDTwB9AF6A09YlhX1+8v2se+fBb9gOPfvvq5EpEbMXJPOOS/Mp9VDszjnhfnMXJPu65Lkt6z/GMZ3gicjvV/Xf+zriuQEzUqZxYUzLqTLv7pw4YwLmZUyy9clyUlwHjrE7pv/hCsjo0aet3v3bjp16lQjz/qlBQsWcNlllwHw5Zdf8sILLxznjt/WsmVLOnfuTNeuXenZs2dNlSgiv0N15tD1BnYYY1KMMeXAR8AfqjYwxuw2xqwHPL+4dwgwxxiTbYzJAebA/7d379FRlecex79PAiURRaxBGwg28YCES9IEkMVFWEg1aDlg8FhrBRWXS1goVy0Wu5YSPViFBtuTYiocDwJLlxxF8IKeQhfisaGeQmJiSAiKKA2BILcGSRqB4Hv+mCFNIJeBZNiZ4fdZK2tmv7Mvz8y7Z7Kf/b773dzSCnF7p/xT2P6Wr3WuY4zX0Yi02Fv5e3l8zTb2VlTjgL0V1Ty+ZlvQk7qf/OQnVFRUUFFRQXZ2dm153QOPtiQjI4Nu3bqRkpJCSkoK77///oUPovB1eHcGHN0DON/juzOCntSFWl298cYb9O3bl4iICHJzc+u99uyzz9KjRw969erF+vXrL3hs7335Hhl/yaC8qhyHo7yqnIy/ZAQ9qQu1Omzq++Z1HZ52KPsPVOfl+a6rDyHjxo1j7tyWDea2adMmCgoKzvp+iYg3Auly2Q3YU2e6DF+LWyAaWvasPopmNhmYDHDNNdcEuGqPfDAfojrD0GleRyISkKfeLWb7vm8afT2/tIITp+qfi6k+eYrHVhfy2pbSBpfp07UT88b2bVFcpw/Qdu/eTXZ2Ng899FCL1nc+ampqaNcu8EuJZ8+ezS9+8YvgBfQ/c2H/tsZfL9sKp47XLztZDW9Pg7xGerP/IAluPf+z8RB6ddWvXz/WrFnDlClT6pVv376dVatWUVxczL59+7jpppv4/PPPiYyMbLU4F2xZwI4jOxp9vfBgISe+O1Gv7NtT3/Lk5idZ/fnqBpdJ/H4ivxz0yxbFFWp1CA1/3y5EHe7/9a85XtJ4HQK4EyeoLiwE56hYtYrjJSVYE0P0d+idyA9+9atmt11TU8N9991Hfn4+1113HStXriQzM5N3332X6upqhg4dypIlSzAzsrKyePHFF2nXrh19+vRh1apVVFVVMX36dLZt20ZNTQ0ZGRncdlu9c/AsX76c3NxcFi9ezKRJk+jUqRO5ubns37+fhQsXcscddwDwm9/8htdff53jx48zfvx4nnrqqQA+PRHxQiAtdA1d2eoCXH9AyzrnljrnBjrnBnbp0iXAVXug9P9g5wa4YRZEXe51NCKt4sxkrrnyQC1cuJCsrCzAd2A2atQoADZu3MjEiROJj4/n0KFDzJ07l127dpGSksKcOb5BhiorK7njjjtITExkwoQJONf4T058fDzz5s2jf//+JCUlsWOH70DsyJEjpKenk5yczODBgyksLAR8Z/4nT55MWloa9957L8uXLyc9PZ2xY8eSkJDA4sWLef7550lNTWXw4MEcOXKkRZ9DqzozmWuuPEDhVle9e/emV69eZ23/7bff5q677qJDhw4kJCTQo0cPtmzZ0qLP7lydmcw1Vx6ocKvDxrSFOgQ4sW9f/em9rdOj4bPPPmPy5MkUFhbSqVMnsrOzmTZtGlu3bqWoqIjq6mrWrVsHwHPPPUd+fj6FhYW8+OKLADzzzDOMGjWKrVu3smnTJubMmUNVVVWT2ywvLycnJ4d169bVttxt2LCBnTt3smXLFgoKCsjLy+Ojjz4CfAOepKWlMWDAAJYuXdoq71tEWiaQU2VlQPc603HAvkbmbWjZkWcs+2GAy7YtzsHGp+HSq2HQZK+jEQlYcy1pw577gL0V1WeVd+sczX9PGXLe2x0xYgSLFi1ixowZ5Obmcvz4cU6ePElOTg7Dhw8nJycH8B2UFBUVUVBQAPi6gOXn51NcXEzXrl0ZNmwYmzdv5oYbbmh0WzExMXzyySdkZ2eTmZnJSy+9xLx580hNTeWtt97igw8+4N57763dRl5eHjk5OURHR7N8+XKKiorIz8/n22+/pUePHixYsID8/Hxmz57NypUrmTVrFgCLFy9m5cqVDBw4kEWLFnHFFa18SXBzLWm/7efvbnmGy7vD/effZS8c66ohe/fuZfDgwbXTcXFx7G2lA/HTmmtJS1udRnlV+VnlsR1jefmWl897u+FYhw193y5EHTbXknbywAF23ZzmOy4AcI7vvvmGbs8v8t2jtgW6d+/OsGHDAJg4cSJZWVkkJCSwcOFC/vGPf3DkyBH69u3L2LFjSU5OZsKECaSnp5Oeng74ErF33nmHzMxMwHcLm9LShntanJaenk5ERAR9+vTh66+/rl3Phg0bSE1NBXxJ/86dOxkxYgSbN2+ma9euHDhwgJtvvpnExERGjBjRovctIi0TSAvdVqCnmSWY2feAu4B3Alz/eiDNzK7wD4aS5i8LHacHIHjqCvjbZviXUfC9jl5HJdJq5ozuRXT7+t2VottHMmf02S0c52LAgAHk5eVx7NgxOnTowJAhQ8jNzeXPf/4zw4cPb3LZQYMGERcXR0REBCkpKezevbvJ+W+//fbabZ6eNycnh3vuuQeAUaNGcfjwYY4ePQr4riGJjo6uXf7GG2/ksssuo0uXLlx++eWMHTsWgKSkpNr1TZ06lV27dlFQUEBsbCyPPvrouX4kLffjJ6F9dP2y9tG+8hYIt7pqTEMtTxd6ePWZ/WcSFRlVrywqMoqZ/We2aL3hVoeNfd/aQh02eY/aFjrzvZgZDz30EKtXr2bbtm08+OCDtfd3e++993j44YfJy8tjwIAB1NTU4JzjzTffpKCggIKCAkpLS+ndu3eT2+zQocM/34f/83XO8fjjj9eu54svvuCBBx4AoGvXrgBcddVVjB8/3pMWUhGpr9mEzjlXA0zDl4iVAK8754rN7GkzGwdgZtebWRnwU2CJmRX7lz0C/Du+pHAr8LS/LDScOQAB+AZE0ahyEkbSU7vx7O1JdOscjeFrmXv29iTSU1t2S4727dsTHx/Pyy+/zNChQxk+fDibNm1i165d53SAERkZSU1NTUDz1523qQO/jh3rn5Spu72IiIja6YiIiNr1XX311URGRhIREcGDDz7ozUFM8p0wNsvXIof5Hsdm+cpbINzqqjFxcXHs2fPPFs6ysrLag9MLZcy1Y8gYmkFsx1gMI7ZjLBlDMxhz7ZgWrTfc6rCx71tbqMMm71HbQqWlpXz88ccAvPbaa7UtpTExMVRWVrJ6te86y++++449e/Zw4403snDhQioqKqisrGT06NH8/ve/r62P/POMafTo0SxbtozKykrA17p94MABqqqqOHbsGABVVVVs2LAhaCNzikjgAro62Tn3PvD+GWVP1nm+FV93yoaWXQYsa0GM3tn4tG/AgbpOVvvKW3gAJdKWpKd2a3EC15ARI0aQmZnJsmXLSEpK4pFHHmHAgAH1zkJfdtlltQcIrb3tV199lSeeeIIPP/yQmJgYOnXqdN7rKy8vJzY2FoC1a9d6dxCTfGdQfn/Cqa4aM27cOO6++24eeeQR9u3bx86dOxk0aFCrb6c5Y64d0+IEriHhVIeNfd/aQh02eo/aVtC7d29WrFjBlClT6NmzJ1OnTuXvf/87SUlJxMfHc/311wNw6tQpJk6cyNGjR3HOMXv2bDp37swTTzzBrFmzSE5OxjlHfHx87TV35yItLY2SkhKGDPF1u7/00kt55ZVXqKysZPz48YBvAJe7776bW24J7cHLRcJB4MNNXYyOlp1buYjUM3z4cJ555hmGDBlCx44diYqKOqv715VXXsmwYcPo168ft956K2PGtM6BbkZGBvfffz/JyclccsklrFjRyCiQAXrssccoKCjAzIiPj2fJkiWtEmdbEU51tXbtWqZPn87BgwcZM2YMKSkprF+/nr59+3LnnXfSp08f2rVrxwsvvNCqoyN6LZzqsLHvWzjXYXx8PNu3bz+rfP78+cyfP/+s8tPXRdYVHR3d4G/TyJEjGTlyJACTJk1i0qRJgG/Ey7pOt8gBzJw5k5kzz+4K/Omnnzb1NkTEA9bUaFZeGDhwoGsz9zVpagCC2UUXPh6RAJWUlDTbzUpERCRY9H9IpGXMLM85NzCQeQMZFOXiFaQBCERERERERFqDulw25fR1Khuf9nWzvDzOl8zp+jmRC278+PF89dVX9coWLFjA6NGjPYpIGqO6Cn2qQxGR0KEulyJhqKSkhMTExAs+nLeIiIhzjh07dqjLpUgLqMulyEUuKiqKw4cPNziUuIiISLA45zh8+DBRUVHNzywirUJdLkXCUFxcHGVlZRw8eNDrUERE5CITFRVFXFyDd7MSkSBQQicShtq3b09CQoLXYYiIiIhIkKnLpYiIiIiISIhSQiciIiIiIhKilNCJiIiIiIiEqDZ32wIzOwj8zes4GhADHPI6CAlr2sckmLR/STBp/5Jg0v4lwdRW968fOue6BDJjm0vo2iozyw30XhAi50P7mAST9i8JJu1fEkzavySYwmH/UpdLERERERGREKWETkREREREJEQpoQvcUq8DkLCnfUyCSfuXBJP2Lwkm7V8STCG/f+kaOhERERERkRClFjoREREREZEQpYROREREREQkRCmhC4CZ3WJmn5nZF2Y21+t4JHyYWXcz22RmJWZWbGYzvY5Jwo+ZRZpZvpmt8zoWCT9m1tnMVpvZDv9v2RCvY5LwYWaz/f8fi8zsNTOL8jomCV1mtszMDphZUZ2y75vZn8xsp//xCi9jPB9K6JphZpHAC8CtQB/g52bWx9uoJIzUAI8653oDg4GHtX9JEMwESrwOQsLWfwB/dM4lAj9C+5q0EjPrBswABjrn+gGRwF3eRiUhbjlwyxllc4GNzrmewEb/dEhRQte8QcAXzrkvnXMngFXAbR7HJGHCOVfunPvE//wYvgOhbt5GJeHEzOKAMcBLXsci4cfMOgEjgP8CcM6dcM5VeBuVhJl2QLSZtQMuAfZ5HI+EMOfcR8CRM4pvA1b4n68A0i9oUK1ACV3zugF76kyXoQNuCQIziwdSgb96G4mEmd8BjwHfeR2IhKVrgYPAy/5uvS+ZWUevg5Lw4JzbC2QCpUA5cNQ5t8HbqCQMXe2cKwffiXbgKo/jOWdK6JpnDZTpXg/SqszsUuBNYJZz7huv45HwYGb/ChxwzuV5HYuErXZAf+APzrlUoIoQ7K4kbZP/WqbbgASgK9DRzCZ6G5VI26OErnllQPc603GouV9akZm1x5fMveqcW+N1PBJWhgHjzGw3vu7io8zsFW9DkjBTBpQ55073LFiNL8ETaQ03AV855w46504Ca4ChHsck4edrM4sF8D8e8Diec6aErnlbgZ5mlmBm38N3Me47HsckYcLMDN+1JyXOuee9jkfCi3PucedcnHMuHt9v1wfOOZ3dllbjnNsP7DGzXv6iHwPbPQxJwkspMNjMLvH/v/wxGnRHWt87wH3+5/cBb3sYy3lp53UAbZ1zrsbMpgHr8Y2utMw5V+xxWBI+hgH3ANvMrMBf9ivn3PsexiQici6mA6/6T3p+CdzvcTwSJpxzfzWz1cAn+EaFzgeWehuVhDIzew0YCcSYWRkwD3gOeN3MHsB3EuGn3kV4fsw5XQ4mIiIiIiISitTlUkREREREJEQpoRMREREREQlRSuhERERERERClBI6ERERERGREKWETkREREREJEQpoRMRkbBlZqfMrKDO39xWXHe8mRW11vpERETOh+5DJyIi4azaOZfidRAiIiLBohY6ERG56JjZbjNbYGZb/H89/OU/NLONZlbof7zGX361ma01s0/9f0P9q4o0s/80s2Iz22Bm0Z69KRERuSgpoRMRkXAWfUaXy5/Vee0b59wgYDHwO3/ZYmClcy4ZeBXI8pdnAf/rnPsR0B8o9pf3BF5wzvUFKoB/C/L7ERERqcecc17HICIiEhRmVumcu7SB8t3AKOfcl2bWHtjvnLvSzA4Bsc65k/7ycudcjJkdBOKcc8frrCMe+JNzrqd/+pdAe+fc/OC/MxERER+10ImIyMXKNfK8sXkacrzO81Po2nQREbnAlNCJiMjF6md1Hj/2P/8LcJf/+QQgx/98IzAVwMwizazThQpSRESkKTqTKCIi4SzazArqTP/ROXf61gUdzOyv+E5u/txfNgNYZmZzgIPA/f7ymcBSM3sAX0vcVKA86NGLiIg0Q9fQiYjIRcd/Dd1A59whr2MRERFpCXW5FBERERERCVFqoRMREREREQlRaqETEREREREJUUroREREREREQpQSOhERERERkRClhE5ERERERCREKaETEREREREJUf8PJB1G0PGG/7EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plot_training_history('Training accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "plt.subplot(2, 1, 2)\n",
    "plot_training_history('Validation accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 2:\n",
    "Describe the results of this experiment. What does this imply about the relationship between batch normalization and batch size? Why is this relationship observed?\n",
    "\n",
    "## Answer:\n",
    "\n",
    "- batch越大训练集的准确率越大\n",
    "- 但在验证集上好像没啥区别，会不会是实现错了orz\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization\n",
    "Batch normalization has proved to be effective in making networks easier to train, but the dependency on batch size makes it less useful in complex networks which have a cap on the input batch size due to hardware limitations. \n",
    "\n",
    "Several alternatives to batch normalization have been proposed to mitigate this problem; one such technique is Layer Normalization [2]. Instead of normalizing over the batch, we normalize over the features. In other words, when using Layer Normalization, each feature vector corresponding to a single datapoint is normalized based on the sum of all terms within that feature vector.\n",
    "\n",
    "[2] [Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. \"Layer Normalization.\" stat 1050 (2016): 21.](https://arxiv.org/pdf/1607.06450.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 3:\n",
    "Which of these data preprocessing steps is analogous to batch normalization, and which is analogous to layer normalization?\n",
    "\n",
    "1. Scaling each image in the dataset, so that the RGB channels for each row of pixels within an image sums up to 1.\n",
    "2. Scaling each image in the dataset, so that the RGB channels for all pixels within an image sums up to 1.  \n",
    "3. Subtracting the mean image of the dataset from each image in the dataset.\n",
    "4. Setting all RGB values to either 0 or 1 depending on a given threshold.\n",
    "\n",
    "## Answer:\n",
    "\n",
    "- 1和2类似LN，3类似BN\n",
    "- 4...？None of above？\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization: Implementation\n",
    "\n",
    "Now you'll implement layer normalization. This step should be relatively straightforward, as conceptually the implementation is almost identical to that of batch normalization. One significant difference though is that for layer normalization, we do not keep track of the moving moments, and the testing phase is identical to the training phase, where the mean and variance are directly calculated per datapoint.\n",
    "\n",
    "Here's what you need to do:\n",
    "\n",
    "* In `cs231n/layers.py`, implement the forward pass for layer normalization in the function `layernorm_forward`. \n",
    "\n",
    "Run the cell below to check your results.\n",
    "* In `cs231n/layers.py`, implement the backward pass for layer normalization in the function `layernorm_backward`. \n",
    "\n",
    "Run the second cell below to check your results.\n",
    "* Modify `cs231n/classifiers/fc_net.py` to add layer normalization to the `FullyConnectedNet`. When the `normalization` flag is set to `\"layernorm\"` in the constructor, you should insert a layer normalization layer before each ReLU nonlinearity. \n",
    "\n",
    "Run the third cell below to run the batch size experiment on layer normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before layer normalization:\n",
      "  means:  [-59.06673243 -47.60782686 -43.31137368 -26.40991744]\n",
      "  stds:   [10.07429373 28.39478981 35.28360729  4.01831507]\n",
      "\n",
      "After layer normalization (gamma=1, beta=0)\n",
      "  means:  [-4.81096644e-16  0.00000000e+00  7.40148683e-17 -5.55111512e-16]\n",
      "  stds:   [0.99999995 0.99999999 1.         0.99999969]\n",
      "\n",
      "After layer normalization (gamma= [3. 3. 3.] , beta= [5. 5. 5.] )\n",
      "  means:  [5. 5. 5. 5.]\n",
      "  stds:   [2.99999985 2.99999998 2.99999999 2.99999907]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after layer normalization   \n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 =4, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before layer normalization:')\n",
    "print_mean_std(a,axis=1)\n",
    "\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "# Means should be close to zero and stds close to one\n",
    "print('After layer normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=1)\n",
    "\n",
    "gamma = np.asarray([3.0,3.0,3.0])\n",
    "beta = np.asarray([5.0,5.0,5.0])\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "print('After layer normalization (gamma=', gamma, ', beta=', beta, ')')\n",
    "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  1.433615657860454e-09\n",
      "dgamma error:  4.519489546032799e-12\n",
      "dbeta error:  2.276445013433725e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "ln_param = {}\n",
    "fx = lambda x: layernorm_forward(x, gamma, beta, ln_param)[0]\n",
    "fg = lambda a: layernorm_forward(x, a, beta, ln_param)[0]\n",
    "fb = lambda b: layernorm_forward(x, gamma, b, ln_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = layernorm_forward(x, gamma, beta, ln_param)\n",
    "dx, dgamma, dbeta = layernorm_backward(dout, cache)\n",
    "\n",
    "#You should expect to see relative errors between 1e-12 and 1e-8\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization and batch size\n",
    "\n",
    "We will now run the previous batch size experiment with layer normalization instead of batch normalization. Compared to the previous experiment, you should see a markedly smaller influence of batch size on the training history!\n",
    "\n",
    "- fortunately, I did see the markedly difference ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No normalization: batch size =  5\n",
      "Normalization: batch size =  5\n",
      "Normalization: batch size =  10\n",
      "Normalization: batch size =  50\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ln_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('layernorm')\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plot_training_history('Training accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "plt.subplot(2, 1, 2)\n",
    "plot_training_history('Validation accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 4:\n",
    "When is layer normalization likely to not work well, and why?\n",
    "\n",
    "1. Using it in a very deep network\n",
    "2. Having a very small dimension of features\n",
    "3. Having a high regularization term\n",
    "\n",
    "\n",
    "## Answer:\n",
    "\n",
    "- may be 2, consider case where hidden_dim equals 1\n",
    "- may be referring to the paper memtioned above would be helpful, but I've not read it yet.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
